Search the finance research feed.
ARXIV · 2026 · arXiv
Understanding similarity among financial assets is essential for effective portfolio diversification. This paper proposes a novel sentiment-adjusted portfolio optimization framework that integrates Topological Data Analysis (TDA) with technical indicators and FinBERT-based sentiment scores extracted from financial news. A TDA-based distance measure is employed within an agglomerative clustering framework to identify topologically dissimilar assets for portfolio construction. By incorporating sentiment information, the framework captures rapid changes in market perception and investor behavior that are not reflected by technical indicators alone. Unlike conventional correlation and Euclidean distance based approaches, the proposed method characterizes complex nonlinear relationships through topological summaries. To account for the transient nature of market sentiment, a dynamic rolling-window rebalancing strategy with frequent portfolio updates is adopted. A retention mechanism is further introduced to preserve high-quality assets across consecutive rebalancing windows, thereby reducing portfolio turnover and transaction costs. Extensive empirical analysis on S&P 500 constituents demonstrates that the proposed framework consistently outperforms correlation and Euclidean distance based methods, as well as benchmark strategies including Naïve, Index, and full-universe portfolios, in terms of returns and reward-risk performance. Furthermore, the framework exhibits strong robustness by delivering positive performance during periods of heightened market uncertainty, such as the U.S.-Israel-Iran conflict.
Authors: Divyanee Garg
Citations: N/A
portfolio constructionq-fin
ARXIV · 2026 · arXiv
We provide a large-scale empirical audit of DEX routing using 2.98 million WETH-USDC swaps on Ethereum. Comparing realized routes with optimized benchmarks, we measure an average shortfall of 2.02 bps per trade or \$24 million. To attribute losses, we introduce three reproducible optimal benchmarks: a Support-Constrained Optimum (SCO) that evaluates split quality conditional on the pools actually used; a Full-Venue Optimum (FVO) that considers all available pools to quantify the value of broader pool access; and a Gas-Aware FVO (G-FVO) that augments FVO with gas costs to capture the trade-off between additional pool usage and gas expenditure. Computing these benchmarks at scale is enabled by a bisection-based algorithm for optimal routing across multiple pools for the same token pair. Two regularities emerge. First, information timeliness is crucial: moving from execution-time state to one-block lagged state optimization significantly raises mean shortfall and additional delays further degrade performance, albeit with diminishing increments; evaluated on the same stale snapshots, realized routes lie closer to optimal, indicating timing-mismatch as a key component. Second, inefficiency is heterogeneous and heavy-tailed: small trades suffer higher percentage losses, while a few extreme outliers dominate the aggregate dollar shortfalls. Finally, we demonstrate that sandwiching attacks drive a significant fraction of routing sub-optimality. Our benchmark protocol and algorithm offer a rigorous, reproducible basis for evaluating and improving information-timely, gas-aware routing.
Authors: Weiye Xi, Ciamac C. Moallemi
Citations: N/A
market microstructureq-finquantitative trading
ARXIV · 2026 · arXiv
We test whether five widely promoted retail signal families - trend, oscillator, candlestick, volume, and calendar rules - deliver a positive, economically meaningful, net-of-cost, and survivable edge. Practical viability is the conjunction of three predeclared gates: statistical edge after multiplicity correction, economic viability after trading costs, and finite-bankroll survival under leverage. Exposure-matched benchmarks, stationary-bootstrap confidence intervals, hierarchical Benjamini-Yekutieli control, one-sided claim-exclusion tests, and equivalence tests distinguish positive evidence, statistically refuted materiality, and unresolved cases. Four of six candidates - oscillator, volume, calendar, and candlestick - are REFUTED, ruled out on statistical and/or economic materiality grounds; trend and a momentum calibration benchmark are INCONCLUSIVE, with confidence intervals too wide at this sample size to resolve the claim; none is SUPPORTED. Cross-sectional tests use point-in-time membership and delisting corrections. The momentum benchmark itself does not clear the statistical gate and is classified INCONCLUSIVE, not REFUTED - the critical validity signature that a genuinely uncertain positive control is never falsely falsified by this design. Under FINRA- and ESMA-anchored leverage and margin scenarios, survival is not the binding constraint for any tested family at the US headline scenario, though it becomes discriminating for trend and oscillator under the higher-leverage EU CFD scenario. The results reject specific promoted deployability claims where confidence bounds rule out the declared effect threshold and classify the remaining cases as unresolved rather than treating non-significance as proof.
Authors: Adam Darmanin
Citations: N/A
market microstructureq-finquantitative trading
ARXIV · 2026 · arXiv
Gaussian Boson Sampling (GBS) provides a native photonic quantum heuristic for sampling dense subgraphs from adjacency matrices, offering a scalable physical approach to combinatorial graph search problems. Simultaneously, correlation matrix clustering algorithms, such as Spectral and SPONGE, have established robust benchmarks for identifying co-moving assets from correlation matrices in statistical arbitrage (StatArb) strategies. In this work, we map S&P 500 residual correlation data into GBS-compatible adjacency matrices. We benchmark those classical clustering algorithms against two quantum clustering algorithms, GBS Boost and our novel GBS Roots, to construct dynamic, market-neutral portfolios over a rolling one-year window. Simulations across distinct macroeconomic regimes reveal that quantum clustering generates superior alpha within large stock universes during periods of high volatility, effectively isolating structural market idiosyncrasies. Crucially, this economic advantage persists under simulated low-loss conditions and extends into high-loss regimes via the application of coherent displacement to compensate for photon loss. Our findings underscore the efficacy of GBS-derived graph clustering in constructing robust StatArb portfolios, establishing a quantum foundation for broader quantitative finance applications.
Authors: Dayne Marcus Lopena, Daniel Buguks, Zhenghao Li, Ewan Mer, Shana H. Winston, Shang Yu, Mihai Cucuringu, Del Rajan
Citations: N/A
statistical arbitrageq-fin
ARXIV · 2026 · arXiv
We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance Spot paper policies after assumed costs. Numerical results come from scripted fixed-seed model runs and deterministic simulators; human-supervised AI agents supported the July 20 evidence-integrity revision through literature retrieval, separately tasked critique, artifact reconciliation, documentation, and source packaging, not trading decisions. The strongest later-period evidence, conditional on extensive predecessor search, is negative: an unchanged ten-pair mandatory-daily selector lost 6.72\% over 19 July cycles at an assumed 31-bps completed-cycle cost, with 3 wins and 16 losses. In short model-specific July evaluations, the validation-selected local-minimum policy returned -1.79\%, while the local-maximum sell-to-cash/re-entry policy underperformed continuous holding by 2.80\%; their gross mean advantages of 11.11 and 12.21 bps were below even the 21-bps stress. A Gurgul-inspired, OHLCV-only daily adaptation attained minimum/maximum ROC AUC of 0.874/0.896 but average precision of only 0.134/0.116 and lost 44.30\% over seven cycles, versus -41.20\% for buy-and-hold. A forensic audit also downgraded an earlier One4All "30-day holdout": its dates had influenced prior architecture work, its four-hour outcome horizon was not purged at split boundaries, it used same-close entry, and its raw result directories were absent. Across the tested, mostly exploratory protocols, event-ranking performance did not establish positive executable policy value. Every operational decision remains NO\_TRADE.
Authors: Ayoub Jadouli
Citations: N/A
market microstructureq-finquantitative trading
ARXIV · 2026 · arXiv
Prediction markets are attracting growing attention as trading volumes rise and their practical relevance increases. To ensure efficient price discovery, liquidity provision becomes ever more important. Due to the binary settlement structure in prediction markets, optimal market making leads to an optimization problem that is fundamentally different from the ones studied in classical settings. In this paper, we develop a stochastic control framework for prediction markets in which the market price is modeled as a conditional probability of the outcome that is generated by a transformed latent belief diffusion. A market maker selects bid and ask quotes to maximize expected terminal wealth while controlling both mark-to-market inventory risk and the settlement risk of remaining positions at resolution. We derive the associated Hamilton--Jacobi--Bellman equation and characterize the unique optimal bid and ask quotes. By transforming the equation to the latent belief space and using a fixed-point argument, we prove existence and uniqueness of a classical solution and verify the resulting optimal quoting strategy. In addition, we provide a numerical analysis, which reveals how optimal liquidity provision in prediction markets depends on inventory, market beliefs, time to resolution, and risk aversion. Further, we demonstrate that the optimal quoting strategy substantially improves downside protection while preserving most of its expected profit relative to a myopic benchmark that maximizes the instantaneous expected mark-to-market profit.
Authors: Dominik Feil, Max Nendel
Citations: N/A
market microstructureq-finquantitative trading
ARXIV · 2026 · arXiv
Automated market makers (AMMs) for prediction markets descend from market scoring rules, where a mechanism operator subsidizes a market to aggregate beliefs about uncertain events. The existing literature has focused on bounding the total worst-case loss to the subsidizer, but has not addressed how that loss is distributed across price states or over time. We use the framework of loss-versus-rebalancing (LVR) to study this distribution and introduce \textit{uniform AMMs}, defined by the property that instantaneous LVR is proportional to pool value and independent of the current token price. In a static setting, we show that for a broad class of \textit{win-martingales} -- processes that converge to 0 or 1 at a fixed resolution time -- there exists a pricing function that achieves uniform LVR under that process, and conversely, that any sufficiently regular pricing function induces a win-martingale under which it is uniform. We then extend the framework to dynamic liquidity management, showing that liquidity levels can be adjusted over time to implement a prescribed target expected cumulative loss schedule. This theory is illustrated with canonical examples of win-martingales and pricing functions. Our results can inform AMM designers and liquidity providers on how the inevitable cost of subsidizing price discovery can be shaped and controlled across both price and time.
Authors: Ciamac C. Moallemi, Dan Robinson, Brian Zhu
Citations: N/A
market microstructureq-finquantitative trading
ARXIV · 2026 · arXiv
An order-book market whose liquidity provision is anchored to a fundamental value carries a restoring force: the price mean-reverts to value and the book refills after a shock. We show this restoring force is a robust intrinsic stabiliser and identify it causally-dialling the anchor down removes the mean-reversion, and a leverage-driven fire-sale then self-sustains. Separately, we ask whether a stressed market transmits its liquidity stress to a coupled calmer one, and find that it cannot: across six transmission channels of increasing strength-cross-market herding, arbitrage flow, and market-maker withdrawal up to a funding-constrained population fire-sale and a leverage spiral-the receiver's stress is independent of whether its neighbour is stressed, at every anchor strength. Market-maker withdrawal thins the receiving book but does not ignite it. Our order parameter throughout is the one-sidedness of the book-liquidity stress rather than a directional price crash-so a liquidity crisis here means the sustained one-sidedness a failing anchor produces. A liquidity crisis in this model is a failure of fundamental anchoring, not of market making.
Authors: Jan Novotny
Citations: N/A
market microstructureq-finquantitative trading
ARXIV · 2026 · arXiv
In this paper, we develop an open-economy macroeconomic model of a Proof-of-Stake network to analyze nominal token-price dynamics and the systemic effects of speculative capital. We first consider a network populated solely by active utility users, who finance network activity through a steady exogenous inflow of fiat currency. We prove the existence of a unique, globally asymptotically stable steady-state equilibrium with a well-defined nominal token price and derive a closed-form expression for the network's relaxation time. Calibrating the model using parameters representative of the current Ethereum network, we estimate a relaxation half-life of approximately 46 years. This extreme macroeconomic inertia implies that the token price may remain persistently displaced from its evolving steady-state benchmark, producing sustained price overshooting as the network adjusts to changing fundamentals. We then introduce an Investor class to examine the effects of passive and active speculative capital. We show that passive institutional staking compresses the native staking yield and creates a structural imbalance that systematically raises the nominal token price while shifting consensus ownership away from active utility users. Active speculative capital has a qualitatively different effect. In response to capital shocks, the Consumer class's rigid preference for fiat-denominated consumption generates an endogenous constant-value strategy. This mechanism shifts staked-token ownership from the Investor class toward active utility users, with potentially favorable implications for consensus decentralization.
Authors: Mikhail Perepelitsa
Citations: N/A
market microstructureq-finquantitative trading
ARXIV · 2026 · arXiv
Foster and Viswanathan (1996) extend the discrete-time setting of Kyle (1985) to multiple informed traders who have partial information about the stock's terminal dividend. We resolve two long-standing open problems in this literature. First, we prove that an equilibrium exists in the setting of Foster and Viswanathan (1996). Second, as the number of trading times goes to infinity, we prove that the discrete-time equilibrium converges to the continuous-time equilibrium already proven to exist in Back, Cao, and Willard (2000).
Authors: Jin Choi, Kasper Larsen
Citations: N/A
market microstructureq-finquantitative trading
ARXIV · 2026 · arXiv
Artificial transaction generation remains an important source of potential market manipulation on cryptocurrency exchanges, as it may distort reported liquidity and reduce market transparency. This study proposes a diagnostic framework for detecting unusual trading patterns based on complexity and statistical-structure measures derived from high-frequency trade-level data. The analysis considers log-returns, trading volume, and transaction counts, using tail distributions, autocorrelation functions, multifractal characteristics, approximate entropy, and detrended cross-correlations. The methodology is applied to BTC, ETH, and XRP traded on Binance, Bitget, KuCoin, and Kraken over the period from April 1 to June 30, 2025. The results reveal a pronounced anomaly on Bitget for BTC and ETH after mid-May 2025. The number of transactions increases sharply, but there is no proportional increase in traded volume or return fluctuations. This regime is characterised by numerous low-volume trades, weaker autocorrelations, reduced multifractal organisation, higher short-pattern irregularity, and weaker cross-correlations involving the transaction-count series. These features are consistent with a noise-like component in trading activity and may indicate artificially increased transaction counts, although they do not provide direct proof of wash trading. The findings show that complexity-based indicators can be useful for detecting exchange-specific trading anomalies that remain hidden in price-based measures.
Authors: Jakub Zwydak, Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
Citations: N/A
high frequency tradingq-fin
ARXIV · 2026 · arXiv
Deep generative models are increasingly used as simulators for downstream decision-making under data scarcity, but in risk-sensitive applications their usefulness depends on rare adverse scenarios rather than typical samples. Standard generative objectives prioritize bulk distributional fidelity, leaving low-probability tails vulnerable to localized optimization noise and making tail-dependent functionals unstable under finite simulation budgets. We introduce Diachronic Sample Integration (DSI), a test-time inference framework that ensembles generated samples across checkpoints from a stochastic training trajectory. DSI targets a checkpoint-mixture distribution that averages checkpoint-specific tail fluctuations rather than relying on a single brittle endpoint. We formalize this mechanism through a finite-budget bias-variance theory. Empirically, across multivariate synthetic processes and high-frequency trading data, DSI substantially reduces tail-estimation error compared to single-checkpoint baselines under fixed simulation budgets, outperforming standard diffusion and state-of-the-art tail-aware baselines without modifying the generative objective.
Authors: Shuning Zhao, Patrick Wong, Leran Zhang, Xiaolin Hu
Citations: N/A
high frequency tradingq-fin
ARXIV · 2026 · arXiv
Empirical correlation matrices estimated from financial return time series are contaminated by statistical noise arising from finite sample size, obscuring genuine interactions among assets. We apply spectral decomposition to separate the empirical correlation matrix into a structured component associated with eigenvalues exceeding the Marchenko-Pastur bounds and a random component representing statistical noise. Using daily returns from the NIFTY 200, NIFTY 500, and S&P 500 over 2010-2022, we show that the structured component, constructed from only 10-16 eigenmodes, reproduces the main statistical properties of the full correlation matrix while removing most noise-dominated eigenmodes. Financial networks derived from the structured component exhibit significantly stronger and more stable core-periphery organization than networks constructed from the full or random matrices. Degree-preserving randomization, Kolmogorov-Smirnov, and Wasserstein distance tests confirm a clear statistical separation between structured and random components. We further show that structured networks display pronounced scale-free degree distributions in the Indian markets. As a practical application, portfolios constructed from peripheral assets of the denoised networks consistently outperform portfolios based on unfiltered correlations and standard benchmarks on a risk-adjusted basis, with robustness verified through Monte Carlo subsampling. These results demonstrate that spectral denoising effectively recovers meaningful network structure from noisy financial correlations.
Authors: Imran Ansari, Shashi Jain, Srikanth K. Iyer
Citations: N/A
portfolio constructionq-fin
ARXIV · 2026 · arXiv
We develop a signature-based framework for optimal execution in statistical arbitrage strategies with path-dependent predictive signals. Both the alpha process and the trading speed are modelled as linear functionals of the truncated signature of a time-augmented market path, placing signal generation and execution on the same truncated signature basis. This allows the trading rule to react to the realised history of the signal while accounting for temporary impact, inventory exposure, terminal liquidation, and approximate dollar neutrality. The main contribution is a quadratic reduction theorem: within the class of signature-linear trading speeds, the restricted path-dependent execution problem becomes a finite-dimensional concave quadratic programme in the policy coefficients. After running synthetic experiments under a mean-reverting log-spread model, we find that the fitted policy achieves a higher return on turnover than a classical $z$-score threshold benchmark. We show how the same workflow can be deployed on a historical equity pairs-trading backtest, where the fitted signature policy again outperforms the benchmark in accounting terms.
Authors: Gianmarco Morbelli, Sven Karbach, Mike Derksen
Citations: N/A
statistical arbitrageq-finpairs trading
ARXIV · 2026 · arXiv
This paper compares different methods for forecasting the term structure of U.S. and European zero-coupon government bonds using both traditional econometric and Machine Learning (ML) approaches. We compare classical models (e.g., Dynamic Nelson-Siegel (DNS) and Principal Component Analysis (PCA)) with different Neural Network (NN) architectures, including those inspired by the classical models, on the U.S. Treasury market and bonds issued by the European Central Bank (ECB). To enhance predictive performance, macroeconomic variables are incorporated. The findings for both markets are separately analyzed and compared. To this end, we propose a robust model evaluation framework combining statistical accuracy metrics - such as RMSE, MAE, and directional accuracy - with the economic relevance of a quantitative bond trading strategy. Results show that NNs consistently outperform traditional models in both forecasting accuracy and portfolio performance. For the U.S., the most effective approach is a direct-forecasting NN that incorporates DNS factors to reduce the dimensionality of zero-rate data and an Autoencoder (AE) to extract macroeconomic features, while for Europe, the optimal model is a factor-based NN using PCA-derived zero-rate factors without the integration of macroeconomic variables. Overall, the paper demonstrates how combining traditional modeling approaches with modern ML techniques and evaluation can improve yield curve forecasts and support applications in fixed-income portfolio construction.
Authors: Tobias Lausser, Joao Eduardo Vuolo, Rudi Zagst
Citations: N/A
portfolio constructionq-fin
ARXIV · 2026 · arXiv
Commodity futures can be represented hierarchically, with underlying assets at the upper level and individual futures contracts at the lower level. Entities at each level can be connected by edges reflecting inherent correlations, with cross-level edges capturing contract-to-underlying asset connections. Building on our observations of these structures, we propose a hierarchical graph learning approach for calendar spread (CS) strategies in commodity futures markets, addressing two significant gaps in the machine-learning literature: (i) the absence of learning-based methods for CS strategies in futures markets, and (ii) the lack of consideration of maturity-dependent interrelationships across commodity futures. We first establish the efficacy of CS strategies by analytically showing that CS strategies can possess higher risk-adjusted returns, measured by the information ratio, and lower risk, measured by variance and delta, than long-only strategies. We then introduce a method to convert learning-based predictions into CS positions. Next, we develop a hierarchical graph learning method that predicts futures price movements by utilizing the maturity-dependent interrelationships, thereby yielding a CS trading algorithm. Empirical results on commodity futures markets traded on the Chicago Mercantile Exchange Group demonstrate that our method outperforms benchmark models in both prediction and trading performance. We find that maturity-dependent interrelationships across commodity futures are instrumental in prediction and that CS trading based on hierarchical graph learning is effective for statistical arbitrage.
Authors: Yoonsik Hong, Diego Klabjan
Citations: N/A
statistical arbitrageq-fin
ARXIV · 2026 · arXiv
Automated Market Makers based on concentrated liquidity, such as Uniswap v3, significantly improve capital efficiency but expose Liquidity Providers (LPs) to adverse selection costs, formalized as Loss-Versus-Rebalancing (LVR). While theoretical literature quantifies these costs, the interplay between realistic blockchain microstructure and endogenous pricing mechanisms remains under-explored. This paper develops a granular Agent-Based Model of a Uniswap v3 pool interacting with a stochastic reference market governed by Heston volatility dynamics. The framework incorporates discrete block propagation, mempool latency, and a heterogeneous population of agents, including latency-sensitive arbitrageurs, smart routers, Maximal Extractable Value searchers, and active LPs benchmarked against a frictionless rebalancing strategy. We propose and evaluate dynamic fee schedules driven by volatility and order-flow toxicity proxies intended to compensate LPs for adverse-selection losses. Our simulations investigate the conditions under which LPs can achieve positive hedged Profit and Loss (fees minus LVR). The analysis suggests that dynamic fee adjustments can improve hedged LP profitability mainly by increasing fee income in states associated with stale-price risk. Depending on the configuration, these rules may also affect realized LVR, but the current aggregate results support compensation for LVR more directly than a reduction of LVR itself.
Authors: Daniele Maria Di Nosse, Fabrizio Lillo
Citations: N/A
order flow toxicityq-fin
ARXIV · 2026 · arXiv
We test whether large language models (LLMs) add value in commodity portfolio construction when the information set and implementation rules are held fixed across strategies. A Hawkish Agent (inflation-tightening prior), a Dovish Agent (growth-easing prior), a Debate Agent, and a deterministic z-score Rule Agent each receive identical FRED macro z-scores and route their tilt signals through the same portfolio engine. Across 124 weekly rebalancing dates spanning the 2023 U.S. rate peak and the 2024-2025 soft landing, all three LLM strategies outperform the Rule Agent in Sharpe terms; the Hawkish and Debate Agents record the largest gains (ΔSharpe = +0.044 and +0.040, both p < 0.10 under a block bootstrap) and preserve a net-of-cost advantage over the passive inverse-volatility benchmark at one-way trading costs up to 30 basis points, while the Rule Agent's thin margin over passive disappears at approximately 5 basis points.The Debate Agent does not outperform the best single agent (ΔSharpe = -0.004, p = 0.769); its contribution is bias correction -- averaging out the Dovish Agent's miscalibrated prior -- rather than deliberation-generated return. The performance advantage is concentrated in the soft-landing sub-period, the evaluation window spans a single rate cycle, and the reported $p$-values are unadjusted for multiple comparisons. Within these limits, the results suggest that an LLM acting as a constrained macro-interpretation function can add modest but economically meaningful value over a transparent rule layer, though the margin is small and its persistence beyond this sample is unknown.
Authors: Yiqing Wang, Dehao Dai, Ding Ma, Kerui Geng
Citations: N/A
portfolio constructionq-fin
ARXIV · 2026 · arXiv
Financial decision systems require fast surrogate models for pricing, calibration, hedging, XVA, stress testing, and portfolio optimization. Standard neural surrogates reproduce prices or risk quantities, but downstream tasks depend as much on derivatives: deltas, vegas, curve and credit-spread sensitivities, exposure and objective gradients. We formulate a derivative-informed operator-learning framework in which the learned map -- a neural operator, random-feature operator, or finite-dimensional surrogate -- is trained both to match a high-fidelity pricing or risk operator and to match directional Fréchet derivatives generated on the fly. The framework combines operator learning, adjoint algorithmic differentiation, tangent sensitivity equations, random sketching of Jacobian actions, and no-arbitrage constraints. We derive error bounds showing derivative accuracy controls local stress errors, hedging error, and optimizer instability, and that discrete-time hedging error is also governed by second-order (gamma) accuracy. A Black--Scholes network over eight seeds shows a tuned derivative weight cuts vega error by 40\% and delta error by 15\% while modestly improving prices, but not an unsupervised second-order Greek. Heston and Bates random-feature experiments reduce stochastic-volatility and jump-parameter sensitivity errors by 60--76\%. A random-feature DeepONet/Galerkin operator mapping instantaneous-volatility curves to dense price surfaces reduces out-of-sample JVP error by 44\% and price RMSE by 23\% over eight seeds; it also shows derivative consistency alone does not remove no-arbitrage violations, so economic constraints must be imposed explicitly. The framework gives a disciplined route from value-only surrogates to derivative-aware engines that output differentiable instruments for hedging, risk, and control.
Authors: Miquel Noguer I Alonso
Citations: N/A
credit spreadsq-fin
ARXIV · 2026 · arXiv
This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets. Although classical implementations of the strategy have proven successful in traditional equities, they frequently exhibit rigidity and suffer from severe divergence risks when applied to high-variance environments. To address this need, this research introduces novel concepts. To construct a robust system, we developed a hierarchical "Filter-then-Rank" pair selection methodology and a proprietary "Fixed Risk, Adaptive Mean" execution model. The system employs a Proximal Policy Optimization (PPO) agent with a Long Short-Term Memory (LSTM) layer to govern execution decisions within strict deterministic risk management boundaries. Evaluated on 1-hour interval data from the Binance USD-M Futures market, the optimized RL policy achieved an out-of-sample performance that substantially outperformed the heuristic baseline. A stationary circular block bootstrap robustness check confirms that the agent's risk-adjusted outperformance is statistically significant at the 10 percent level. Although falling marginally short of the stricter 5 percent threshold, this result highlights the extreme idiosyncratic variance characteristic of digital assets. Ultimately, this thesis contributes to the quantitative finance literature by introducing a hybrid architecture that combines statistical arbitrage with DRL execution policies. Furthermore, it delivers a novel framework for safe reinforcement learning via deterministic shielding, proving that anchoring a neural policy to statistically robust boundaries successfully mitigates severe divergence risks.
Authors: Damian Lebiedź, Robert Ślepaczuk
Citations: N/A
statistical arbitrageq-finpairs trading
ARXIV · 2026 · arXiv
We introduce the Polymarket-v1 Database: the complete on-chain trade archive of Polymarket's first-generation CTF Exchange on Polygon, spanning 2022-11-21 to 2026-04-28 and covering the full contract lifecycle from first settlement to natural termination. The dataset comprises 1.20 billion trade records across 1.30 million markets with $61 billion in nominal volume. Its defining feature is 100% ground-truth aggressor direction derived from the blockchain settlement layer, a property unavailable in existing prediction market archives, which rely on heuristic inference. We use this truth-aligned archive to benchmark standard microstructure tools and document three findings. First, the tick rule and bulk volume classification achieve near-random aggregate accuracy (49.83% and 50.51%), but this masks a systematic, correctable price-level gradient driven by positive trade direction autocorrelation and concentrated market-making -- two structural features of prediction markets that violate the mean-reversion assumption embedded in classical classifiers. Second, these classification errors propagate into downstream metrics: inferred VPIN diverges substantially from ground-truth VPIN, and OFI estimates are directionally biased, with material consequences for Transaction Cost Analysis. Third, ground-truth microstructure quality predicts forecasting performance in ways that classification-based proxies cannot recover: True VPIN positively predicts Brier scores, while Gibbs spread negatively predicts them -- a selection effect reflecting that high-spread niche markets attract informed specialists rather than noise traders. Replacing ground-truth metrics with classified proxies attenuates both relationships, illustrating that measurement accuracy at the transaction level is a prerequisite for reliable inference about prediction market design and probability calibration.
Authors: Boka Qin, Rui Yang
Citations: N/A
transaction cost analysisq-fin
ARXIV · 2026 · arXiv
This paper compares a series of contemporary portfolio construction approaches by employing ten U.S. stocks (TSLA, WMT, BAC, GS, LLY, MRK, GOOG, META, AAPL and XOM) in a time frame from September 2023 to December 2025. The paper explores both basic mean-variance optimization, constrained optimization, Fama French five factor regression modeling, Monte Carlo simulation, and the Black-Litterman model to determine how constraints to a solution, risk factors to a strategy, simulated approximations, and specific market views may all impact the outcome of portfolio allocation, performance and stability. Overall, the results show that standard optimization may result in highly concentrated portfolios, while constrained optimization leads to changes in portfolio allocations by altering the efficient frontier, five factor regression models suggest that a basic investment style of defensive large value and profitability exposure, Monte Carlo approximation is a viable technique to arrive at mean-variance optimal portfolios provided the simulations are high enough especially under a box constraint, the Black Litterman portfolio approach produces more economically intuitive allocations and greater stability compared to standard mean-variance optimization as the approach balances equilibrium returns with investor views.
Authors: Ajay Kumar Verma, Shravya Barkam
Citations: N/A
portfolio constructionq-fin
ARXIV · 2026 · arXiv
This paper develops a three-currency Heath-Jarrow-Morton framework in which corporate credit is treated as a separate economy, connected to the nominal and real economies through synthetic inflation and credit exchange rates. The framework produces a testable identity. Under joint no-arbitrage, the credit spread of an issuer expressed over the inflation-rateindexed risk-free curve equals the same issuer's credit spread expressed over the nominalrate-indexed risk-free curve plus the model-implied breakeven inflation forward at the same maturity. The identity holds within any single calibration of the framework. It is empirically falsifiable across two parallel corporate-bond segments of the same market, in a segmented market the two segments may price different corporate credit economies, and the gap between their implied corporate forwards measures the failure of the shared-credit-economy assumption. Applied to Brazilian debenture markets, the framework delivers a sharp empirical finding. Fifteen large issuers placed paper in both the CDI-indexed general-purpose segment and the IPCA-indexed infrastructure segment between January 2021 and February 2026. The within-issuer triangle residual at the 3-year tenor averages 640 basis points, with crosssectional standard deviation of 26 basis points across the 15 issuer means, and remains stable through both the 2021-2023 BCB tightening cycle and the 2024-2026 easing phase. A retail post-tax indifference benchmark anchored on Lei 12.431 closes the bulk of the residual. The remainder is consistent with institutional participation on the CDI side, contractual asymmetries between debentures with different use-of-proceeds restrictions, and segment-specific liquidity gaps.
Authors: Raphael Coelho
Citations: N/A
credit spreadsq-fin
ARXIV · 2026 · arXiv
Predicting cross-sectional stock returns is challenging due to low signal-to-noise ratios and evolving market regimes. Classical factor models offer interpretability but limited flexibility, while deep learning models achieve strong performance yet often underutilize financial priors. We address this gap with PRISM-VQ (PRior-Informed Stock Model with Vector Quantization), a dynamic factor framework that integrates expert prior factors, vector-quantized discrete latent factors learned from cross-sectional structure, and a structure-conditioned Mixture-of-Experts to generate time-varying factor loadings. Vector quantization acts as an information bottleneck that suppresses noise while capturing robust market structure, with discrete codes serving both as latent factors and as routing signals for temporal expert specialization. Experiments on CSI 300 and S&P 500 show consistent improvements in cross-sectional return prediction and portfolio performance over strong baselines while preserving interpretability. Our code is available at https://github.com/finxlab/PRISM-VQ.
Authors: Namhyoung Kim, Jae Wook Song
Citations: N/A
portfolio constructionq-fin
ARXIV · 2026 · arXiv
Estimating the covariance of asset returns, i.e., the risk model, is a key component of financial portfolio construction and evaluation. Most risk modeling approaches produce a factor model that decomposes the asset variability into two components: the first attributed to a small number of factors that are common among the assets and the second attributed to the idiosyncratic behavior of each asset. Third-party providers typically provide risk models to investors, and while these models are typically of high quality, they may fail to capture important information, e.g., changing market regimes and transient factors. To overcome these limitations, we propose a systematic method based on maximum likelihood estimation to enhance an existing factor model by both refining the given model and adding new statistical factors. Our approach relies only on the observed sequence of realized returns and on the choice of two hyperparameters: the number of additional factors and the half-life parameter that determines the weights assigned to returns in the log-likelihood objective. Importantly, our methodology applies to the situation where asset returns may be missing, making it suitable for typical equity datasets. We demonstrate our approach on the Barra short-term US risk model, a high-quality risk model used in practice, for a universe of US high-capitalization equities. We show that the proposed extension captures structure in the returns that is missed by the original model.
Authors: Alexandros E. Tzikas, Emmanuel J. Candès, Trevor Hastie, Stephen P. Boyd, Mykel J. Kochenderfer, Ronald N. Kahn
Citations: N/A
portfolio constructionq-fin
ARXIV · 2026 · arXiv
We present a new class of Bayesian dynamic models for bivariate price-realized volatility time series in financial forecasting. A novel dynamic gamma process model adopted for realized volatility is integrated with traditional Bayesian dynamic linear models (DLMs) for asset price series. This represents reduced-form volatility leverage and feedback effects through use of realized volatility proxies in conditional DLMs for prices or returns, coupled with the synthesis of higher frequency data to track and anticipate volatility fluctuations. Analysis is computationally straightforward, extending conjugate-form Bayesian analyses for sequential filtering and model monitoring with simple and direct simulation for forecasting. A main applied setting is equity return forecasting with daily prices and realized volatility from high-frequency, intraday data. Detailed empirical studies of multiple S&P sector ETFs highlight the improvements achievable in asset price forecasting relative to standard models and deliver contextual insights on the nature and practical relevance of volatility leverage and feedback effects. The analytic structure and negligible extra computational cost will enable scaling to higher dimensions for multivariate price series forecasting for decouple/recouple portfolio construction and risk management applications.
Authors: Patrick Woitschig, Mike West
Citations: N/A
portfolio constructionq-fin
ARXIV · 2026 · arXiv
Financial markets such as bond, derivatives, and repo markets form networks of interdependent obligations. Existing multilateral netting methods typically trade off the extent of netting against preservation of counterparty exposure: central clearing reallocates exposure to a central counterparty, while trade compression may alter bilateral counterparty relationships. TradeMech is a mechanism for markets in which one or two homogeneous fungible objects are traded. The mechanism transforms a network of initial bilateral contracts into chains and cycles, nets the designated object multilaterally on those chains and cycles, and replaces initial contracts with multiparty contracts whose assigned trades remain fractions of the original bilateral trades. The construction achieves maximal multilateral netting of the designated object while preserving each agent's contractual profit and preserving the location of counterparty risk. When a party fails to pre-commit a required object, the affected assigned trade is recovered as a bilateral contract between the same original counterparties and the remaining assigned trades are re-netted on residual chains, so no new counterparty exposure is created.
Authors: Daniel Aronoff, Robert M. Townsend, Madars Virza
Citations: N/A
repo marketq-fin
ARXIV · 2026 · arXiv
Persistent shifts in term-structure dynamics undermine the stability of single-regime models in long samples. We develop an arbitrage-free regime-switching generalized CIR (RS-GCIR) model that jointly prices the Chinese government bond (CGB) curve and corporate bond curves. To capture the systematic transmission from interest-rate conditions to credit spreads, we structure the model into two blocks and price corporate bonds conditional on the prevailing rate regime. The rate block features a two-state RS-GCIR short-rate process estimated from CGB zero-coupon curves, while the credit block embeds CIR-type credit factors in an intensity-based framework for rating migration and default. We implement a block-recursive Unscented Kalman Filter (UKF) procedure--filtering the rate block first and the credit block next--using weekly data from 2014--2025, a period that begins with the onset of China's modern corporate default cycle. We identify two persistent rate regimes with distinct level--volatility profiles. Relative to single-regime benchmarks, regime switching improves joint curve fit, delivers economically interpretable filtered regime probabilities, and sharpens the decomposition of corporate yields into discounting and credit compensation.
Authors: Maochun Xu, Yunqi Liang, Yi Hong
Citations: N/A
credit spreadsq-fin
ARXIV · 2026 · arXiv
This paper proposes an Extended State-Dependent Hawkes Process (ExsdHawkes) to model the intricate dynamics of Limit Order Books (LOBs). Our theoretical contribution lies in relaxing traditional constraints by allowing for state disappearances -- a phenomenon frequently observed in high-frequency trading. We mathematically prove, using Karush--Kuhn--Tucker (KKT) conditions, that the maximum likelihood estimation remains separable, justifying an efficient two-step procedure. In the empirical section, we apply our model to three months of high-frequency tick data of Mitsubishi UFJ Financial Group (8306). We demonstrate that ExsdHawkes uniquely reproduces the volatility signature plot's characteristic upward slope by capturing the "local super-criticality" triggered during disequilibrium states. Crucially, we identify Marketable Limit Orders (MLO) as the primary catalyst that forces the LOB into these unstable states. Comparative analysis reveals that models lacking physical constraints (e.g., standard SD-Hawkes) suffer from explosive branching ratios and fail to maintain simulation stability. Our findings suggest that physical consistency is not merely a mathematical nicety, but a prerequisite for accurately modeling macro-level volatility. By enforcing the physical geometry to `pause' the residual accumulation during inadmissible periods, ExsdHawkes uniquely maintains statistical integrity where unconstrained models succumb to structural bias.
Authors: Akitoshi Kimura
Citations: N/A
high frequency tradingq-fin
ARXIV · 2026 · arXiv
We investigate the optimal execution of contracts that are used in merger\&acquisition deals. We consider cash-settled and physically delivered contracts between a broker and a counterpart. Contracts are linear (total returns swaps), nonlinear (collar contracts) or Asian type (TWAP based contracts). We derive the optimal execution strategy and the optimal fee through indifference utility arguments allowing for linear market effects of trades. We show that linear cash-settled contracts are more expensive and more exposed to manipulation/statistical arbitrages by the broker. Also nonlinear and Asian type contracts are exposed to these phenomena.
Authors: Emilio Barucci, Yuheng Lan, Daniele Marazzina
Citations: N/A
statistical arbitrageq-fin
ARXIV · 2026 · arXiv
Scaling generative inverse and forward rendering to real-world scenarios is bottlenecked by the limited realism and temporal coherence of existing synthetic datasets. To bridge this persistent domain gap, we introduce a large-scale, dynamic dataset curated from visually complex AAA games. Using a novel dual-screen stitched capture method, we extracted 4M continuous frames (720p/30 FPS) of synchronized RGB and five G-buffer channels across diverse scenes, visual effects, and environments, including adverse weather and motion-blur variants. This dataset uniquely advances bidirectional rendering: enabling robust in-the-wild geometry and material decomposition, and facilitating high-fidelity G-buffer-guided video generation. Furthermore, to evaluate the real-world performance of inverse rendering without ground truth, we propose a novel VLM-based assessment protocol measuring semantic, spatial, and temporal consistency. Experiments demonstrate that inverse renderers fine-tuned on our data achieve superior cross-dataset generalization and controllable generation, while our VLM evaluation strongly correlates with human judgment. Combined with our toolkit, our forward renderer enables users to edit styles of AAA games from G-buffers using text prompts.
Authors: Zheng-Hui Huang, Zhixiang Wang, Jiaming Tan, Ruihan Yu, Yidan Zhang, Bo Zheng, Yu-Lun Liu, Yung-Yu Chuang
Citations: N/A
creditcredit spread decomposition
ARXIV · 2026 · arXiv
We study Anderson localization in a one-dimensional disordered system with long-range correlated hopping decaying as $1/r^{a}$ with complex hopping amplitudes that break time-reversal symmetry in a tunable fashion by varying their argument. We find analytically a corelation-induced algebraic localization that is robust to a finite strength of the time-reversal-symmetry-breaking parameter, beyond which all states delocalize. This establishes a localization--delocalization transition driven by the interplay between long-ranged correlated hopping and time-reversal symmetry breaking. In addition to obtaining the static localization phase diagram, we also investigate the dynamical phase diagram through the lens of wavepacket spreading. We find that the growth in time of the mean-squared displacement of a wavepacket, which is subdiffusive for the time-reversal symmetric case, becomes diffusive for any finite value of the time-reversal-symmetry-breaking parameter.
Authors: Bikram Pain, Sthitadhi Roy, Jens H. Bardarson, Ivan M. Khaymovich
Citations: N/A
creditcredit spread decomposition
ARXIV · 2026 · arXiv
Retractions serve as an indicator of failures in research integrity, yet most analyses focus on absolute counts rather than risk per paper. We use one of the largest open bibliographic databases to develop incidence metrics normalized by population: retractions per publication and per active author annually. Applying an epidemiological framework that models counts with exposure, we find evidence of exponential growth in retraction incidence, with approximately a 5-year doubling time at both the paper and author levels. These patterns vary significantly across fields, publishers, and countries. While scientific output is becoming more democratized globally, retractions are concentrated in fewer countries, creating a "concentration" paradox that calls for targeted monitoring. Despite exponential growth, the absolute incidence remains low (0.12% in 2021), allowing for corrective intervention. Incidence-based monitoring provides a framework for evaluating policies that safeguard research integrity at scale.
Authors: Sara Venturini, Alessandra Urbinati, Paola Gallo, Jessica T. Davis, Alessandro Vespignani
Citations: N/A
macromonetary policy asset pricingglobal liquidity financial markets
ARXIV · 2026 · arXiv
Recent multimodal large language models have achieved strong performance in unified text and image understanding and generation, yet extending such native capability to 3D remains challenging due to limited data. Compared to abundant 2D imagery, high-quality 3D assets are scarce, making 3D synthesis under-constrained. Existing methods often rely on indirect pipelines that edit in 2D and lift results into 3D via optimization, sacrificing geometric consistency. We present Omni123, a 3D-native foundation model that unifies text-to-2D and text-to-3D generation within a single autoregressive framework. Our key insight is that cross-modal consistency between images and 3D can serve as an implicit structural constraint. By representing text, images, and 3D as discrete tokens in a shared sequence space, the model leverages abundant 2D data as a geometric prior to improve 3D representations. We introduce an interleaved X-to-X training paradigm that coordinates diverse cross-modal tasks over heterogeneous paired datasets without requiring fully aligned text-image-3D triplets. By traversing semantic-visual-geometric cycles (e.g., text to image to 3D to image) within autoregressive sequences, the model jointly enforces semantic alignment, appearance fidelity, and multi-view geometric consistency. Experiments show that Omni123 significantly improves text-guided 3D generation and editing, demonstrating a scalable path toward multimodal 3D world models.
Authors: Chongjie Ye, Cheng Cao, Chuanyu Pan, Yiming Hao, Yihao Zhi, Yuanming Hu, Xiaoguang Han
Citations: N/A
macromonetary policy asset pricing
ARXIV · 2026 · arXiv
The Metaverse faces complex resource allocation challenges due to diverse Virtual Environments (VEs), Digital Twins (DTs), dynamic user demands, and strict immersion needs. This paper introduces CIVIC (Cooperative Immersion Via Intelligent Credit-sharing), a novel framework optimizing resource sharing among multiple Metaverse Service Providers (MSPs) to enhance user immersion. Unlike existing methods, CIVIC integrates VE rendering, DT synchronization, credit sharing, and immersion-aware provisioning within a cooperative multi-MSP model. The resource allocation problem is formulated as two NP-hard challenges: a non-cooperative setting where MSPs operate independently and a cooperative setting utilizing a General Credit Pool (GCP) for dynamic resource sharing. Using Deep Reinforcement Learning (DRL) for tuning resources and managing cooperating MSPs, CIVIC achieves 12-36% higher request completion, 23-70% higher fulfillment rates, 20-60% more served clients, and up to 51% more fairly distributed requests, all with competitive costs. Extensive experiments demonstrate CIVIC's resilience, adaptability, and robust performance under dynamic load conditions and unexpected demand surges, making it suitable for real-world distributed Metaverse infrastructures.
Authors: Amr Aboeleneen, Mohamed Abdallah, Aiman Erbad, Amr Salem
Citations: N/A
creditcredit default swap indexcredit spread decompositiondistressed debt credit markets
ARXIV · 2026 · arXiv
Agent skills, structured packages of procedural knowledge and executable resources that agents dynamically load at inference time, have become a reliable mechanism for augmenting LLM agents. Yet inference-time skill augmentation is fundamentally limited: retrieval noise introduces irrelevant guidance, injected skill content imposes substantial token overhead, and the model never truly acquires the knowledge it merely follows. We ask whether skills can instead be internalized into model parameters, enabling zero-shot autonomous behavior without any runtime skill retrieval. We introduce SKILL0, an in-context reinforcement learning framework designed for skill internalization. SKILL0 introduces a training-time curriculum that begins with full skill context and progressively withdraws it. Skills are grouped offline by category and rendered with interaction history into a compact visual context, teaching he model tool invocation and multi-turn task completion. A Dynamic Curriculum then evaluates each skill file's on-policy helpfulness, retaining only those from which the current policy still benefits within a linearly decaying budget, until the agent operates in a fully zero-shot setting. Extensive agentic experiments demonstrate that SKILL0 achieves substantial improvements over the standard RL baseline (+9.7\% for ALFWorld and +6.6\% for Search-QA), while maintaining a highly efficient context of fewer than 0.5k tokens per step. Our code is available at https://github.com/ZJU-REAL/SkillZero.
Authors: Zhengxi Lu, Zhiyuan Yao, Jinyang Wu, Chengcheng Han, Qi Gu, Xunliang Cai, Weiming Lu, Jun Xiao
Citations: N/A
macromonetary policy asset pricing
ARXIV · 2026 · arXiv
We demonstrate that wave amplification enables even weak nonlinearities to reshape linear wave-packet transport in nonreciprocal systems. We study the dynamics of bulk Gaussian wave packets in the Hatano--Nelson model with onsite cubic nonlinearity. We show that the interplay between nonlinearity and amplification generates growing frequency shifts that drive the wave packet through three successive dynamical regimes: an early nonlinear-skin regime with coherent propagation, an intermediate wave-mixing regime driven by mode resonances, and a self-trapping regime in which part of the packet localizes while the remainder ballistically spreads along the system favored direction. The crossover time scales are set by the width and average spacing of the eigen-frequency spectrum. Crucially, within the nonlinear-skin regime, we derive analytical predictions for the wave-packet dynamics and show that nonlinearity couples amplification, dispersion, and nonreciprocity, thereby modifying the magnitude of the wave-packet acceleration and introducing an explicit time dependence into its evolution. Focusing nonlinearities suppress the acceleration and cause it to decrease in time, whereas defocusing nonlinearities enhance it and cause it to increase. We further show that nonlinear interactions typically break down the wave packet before the non-Hermitian jump can occur. Our results provide a route toward accurate control of waves in nonreciprocal metamaterials.
Authors: Bertin Many Manda, Vassos Achilleos
Citations: N/A
creditcredit spread decomposition
ARXIV · 2026 · arXiv
We study the degree of the Plücker embedding $\varpi$ of the Quot scheme of length $l$ quotients of a locally free sheaf on a smooth projective scheme $\mathrm{S}$ of dimension $d\geqslant 1$. This degree is determined by classes in the Chow ring of the symmetric product $\mathrm{S}^{(l)}$, which are given by the pushforward of the powers of $c_{1}(\mathcal{O}^{[l]})$ with respect to the canonical morphism from the Quot scheme to $\mathrm{S}^{(l)}$. We describe a decomposition of these classes, allowing us to compute the (in a certain sense) leading term of $\mathrm{deg} \ \varpi$. We also obtain a higher-dimensional analogue of a classical result of Schubert.
Authors: Samuel Stark
Citations: N/A
creditcredit spread decomposition
ARXIV · 2026 · arXiv
Multimodal time-to-event prediction often requires integrating sensitive data distributed across multiple parties, making centralized model training impractical due to privacy constraints. At the same time, most existing multimodal survival models produce single deterministic predictions without indicating how confident the model is in its estimates, which can limit their reliability in real-world decision making. To address these challenges, we propose BVFLMSP, a Bayesian Vertical Federated Learning (VFL) framework for multimodal time-to-event analysis based on a Split Neural Network architecture. In BVFLMSP, each client independently models a specific data modality using a Bayesian neural network, while a central server aggregates intermediate representations to perform survival risk prediction. To enhance privacy, we integrate differential privacy mechanisms by perturbing client side representations before transmission, providing formal privacy guarantees against information leakage during federated training. We first evaluate our Bayesian multimodal survival model against widely used single modality survival baselines and the centralized multimodal baseline MultiSurv. Across multimodal settings, the proposed method shows consistent improvements in discrimination performance, with up to 0.02 higher C-index compared to MultiSurv. We then compare federated and centralized learning under varying privacy budgets across different modality combinations, highlighting the tradeoff between predictive performance and privacy. Experimental results show that BVFLMSP effectively includes multimodal data, improves survival prediction over existing baselines, and remains robust under strict privacy constraints while providing uncertainty estimates.
Authors: Abhilash Kar, Basisth Saha, Tanmay Sen, Biswabrata Pradhan
Citations: N/A
creditcredit default swap index
ARXIV · 2026 · arXiv
3GPP Release 19 has initiated the standardization of integrated sensing and communications (ISAC), including a channel model for monostatic sensing, evaluation scenarios, and performance assessment methodologies. These common assumptions provide an important basis for ISAC evaluation, but reproducible end-to-end studies still require a transparent sensing implementation. This paper evaluates 5G New Radio (NR) base station (gNB)-based monostatic sensing for the Unmanned Aerial Vehicle (UAV) use case using a 5G NR downlink Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM) waveform and positioning reference signals (PRS), following 3GPP Urban Macro-Aerial Vehicle (UMa-AV) scenario assumptions. We present an end-to-end processing chain for multi-target detection and 3D localization, achieving more than 70% detection probability with less than 5% false alarm rate, in the considered scenario. For correctly detected targets, localization errors are on the order of a few meters, with a 90th-percentile error of 4m and 6m in the vertical and horizontal directions, respectively. To support reproducible baseline studies and further research, we release the simulator 5GNRad, which reproduces our evaluation
Authors: Steve Blandino, Neeraj Varshney, Jian Wang, Jack Chuang, Camillo Gentile, Nada Golmie
Citations: N/A
macromacro finance
ARXIV · 2026 · arXiv
We present a systematic study of the electronic structure of strained La$_3$Ni$_2$O$_7$ thin films. We show that biaxial compressive strain mainly elongates the outer apical Ni-O bond while leaving the inner apical Ni-O bond nearly unchanged. As a result, the Jahn-Teller splitting $Δ_{JT}$ is strongly enhanced, whereas the interlayer $d_{z^2}$ hopping $t_\perp^z$ changes only weakly. Since superconductivity is widely believed to emerge only below a critical in-plane lattice constant, our results identify the strain-enhanced $Δ_{JT}$ as the relevant microscopic tuning parameter. Consistently, the calculated Fermi surfaces and Hall response for LaAlO$_3$ and SrLaAlO$_4$ substrates agree with ARPES and Hall measurements. Our results identify Jahn-Teller distortion as a key tuning parameter in strained La$_3$Ni$_2$O$_7$ and support its central role in optimizing superconductivity in bilayer nickelates.
Authors: Yuxin Wang, Zhan Wang, Fu-Chun Zhang, Kun Jiang
Citations: N/A
creditcorporate bond liquidity
ARXIV · 2026 · arXiv
As TLS 1.3 encryption limits traditional Deep Packet Inspection (DPI), the security community has pivoted to Euclidean Transformer-based classifiers (e.g., ET-BERT) for encrypted traffic analysis. However, these models remain vulnerable to byte-level adversarial morphing -- recent pre-padding attacks reduced ET-BERT accuracy to 25.68%, while VLESS Reality bypasses certificate-based detection entirely. We introduce AEGIS: an Adversarial Entropy-Guided Immune System powered by a Thermodynamic Variance-Guided Hyperbolic Liquid State Space Model (TVD-HL-SSM). Rather than competing in the Euclidean payload-reading domain, AEGIS discards payload bytes in favor of 6-dimensional continuous-time flow physics projected into a non-Euclidean Poincare manifold. Liquid Time-Constants measure microsecond IAT decay, and a Thermodynamic Variance Detector computes sequence-wide Shannon Entropy to expose automated C2 tunnel anomalies. A pure C++ eBPF Harvester with zero-copy IPC bypasses the Python GIL, enabling a linear-time O(N) Mamba-3 core to process 64,000-packet swarms at line-rate. Evaluated on a 400GB, 4-tier adversarial corpus spanning backbone traffic, IoT botnets, zero-days, and proprietary VLESS Reality tunnels, AEGIS achieves an F1-score of 0.9952 and 99.50% True Positive Rate at 262 us inference latency on an RTX 4090, establishing a new state-of-the-art for physics-based adversarial network defense.
Authors: Vickson Ferrel
Citations: N/A
creditcorporate bond liquidity
ARXIV · 2026 · arXiv
Magnetohydrodynamic (MHD) phenomena play a pivotal role in the design and operation of nuclear fusion systems, where electrically conducting fluids (such as liquid metals or molten salts employed in reactor blankets) interact with magnetic fields of varying intensity and orientation, influencing the resulting flow dynamics. The numerical solution of MHD models entails the resolution of highly nonlinear, multiphysics systems of equations, which can become computationally demanding, particularly in multi-query, parametric, or real-time contexts. This study investigates a fully data-driven framework for MHD state reconstruction that integrates dimensionality reduction through Singular Value Decomposition (SVD) with the SHallow REcurrent Decoder (SHRED), a neural network architecture designed to reconstruct the full spatio-temporal state from sparse time-series measurements of selected observables, including previously unseen parametric configurations. The SHRED methodology is applied to a three-dimensional geometry representative of a portion of a WCLL blanket cell, in which lead-lithium flows around a water-cooled tube. Multiple magnetic field configurations are examined, including constant toroidal fields, combined toroidal-poloidal fields, and time-dependent magnetic fields. Across all considered scenarios, SHRED achieves high reconstruction accuracy, robustness, and generalization to magnetic field intensities, orientations, and temporal evolutions not seen during training. Notably, in the presence of time-varying magnetic fields, the model accurately infers the temporal evolution of the magnetic field itself using temperature measurements alone. Overall, the findings identify SHRED as a computationally efficient, data-driven, and flexible approach for MHD state reconstruction, with significant potential for real-time monitoring, diagnostics and control in fusion reactor systems.
Authors: M. Lo Verso, C. Introini, E. Cervi, L. Savoldi, J. N. Kutz, A. Cammi
Citations: N/A
creditcorporate bond liquidity
ARXIV · 2026 · arXiv
We study a speculative trading problem within the exploratory reinforcement learning (RL) framework of Wang et al. [2020]. The problem is formulated as a sequential optimal stopping problem over entry and exit times under general utility function and price process. We first consider a relaxed version of the problem in which the stopping times are modeled by the jump times of Cox processes driven by bounded, non-randomized intensity controls. Under the exploratory formulation, the agent's randomized control is characterized via the probability measure over the jump intensities, and their objective function is regularized by Shannon's differential entropy. This yields a system of the exploratory HJB equations and Gibbs distributions in closed-form as the optimal policy. Error estimates and convergence of the RL objective to the value function of the original problem are established. Finally, an RL algorithm is designed, and its implementation is showcased in a pairs-trading application.
Authors: Yun Zhao, Alex S. L. Tse, Harry Zheng
Citations: N/A
pairs tradingq-fin
ARXIV · 2026 · arXiv
We present a large-scale experimental study of quantum-computing-based molecular simulation carried out on IQM's Sirius 24-qubit superconducting processor, utilizing up to 16 operational qubits. The work employs Sample-based Quantum Diagonalization (SQD) together with the Local Unitary Cluster Jastrow (LUCJ) ansatz to estimate ground-state energies for a set of benchmark molecules, including H$_2$, LiH, BeH$_2$, H$_2$O, and NH$_3$. In addition, we introduce a Linear-CNOT variant of the Unitary Coupled-Cluster Singles and Doubles (LCNot-UCCSD) ansatz within the SQD workflow, trading higher circuit depth for reduced classical preprocessing. A comparison between these ansätze is provided, clarifying their respective strengths, limitations, and suitability for near-term quantum hardware. We further explore potential energy landscapes through 1D scans for H$_2$ and HeH$^+$ using both STO-3G and 6-31G basis sets, and for LiH and BeH$_2$ in STO-3G. Extending beyond this, we demonstrate the experimental construction of a full 2D potential energy surface for the water molecule on quantum hardware, mapped over a 32 $\times$ 32 grid in bond length and bond angle. To move beyond small benchmark systems, we combine SQD(LUCJ) with Density Matrix Embedding Theory (DMET) to compute active-space energies for a set of ligand-like molecules, as well as the pharmacologically relevant amantadine system. Across all studies, the majority of quantum-computed energies agree with reference FCI results, as well as with DMET-CASCI energies for embedded systems, to within chemical accuracy for the chosen basis sets. These results demonstrate the reliability of sample-based diagonalization approaches and underscore the potential of hybrid embedding strategies for extending quantum simulations to increasingly complex molecular systems, while also highlighting their practicality on current IQM quantum hardware.
Authors: Anurag K. S. V., Ashish Kumar Patra, Manas Mukherjee, Alok Shukla, Sai Shankar P., Ruchika Bhat, Radhika T. S. L., Jaiganesh G
Citations: N/A
creditcorporate bond liquidity
ARXIV · 2026 · arXiv
Protein rotational kinetics are essential for understanding macromolecular behavior in crowded environments, yet measuring these dynamics at solid-liquid interfaces remains a significant challenge due to low signal strengths. Here, we experimentally demonstrate a label-based optical technique for measuring rotational diffusion kinetics using an all-dielectric multilayer stack that sustains both transverse electric and transverse magnetic polarized surface electromagnetic waves. We introduce the concept of Fluorescence Recovery after Orientational Photobleaching, a rotational analogue to the standard translatory fluorescence recovery after photobleaching technique, which utilizes anisotropic photobleaching via resonant transverse electric excitation followed by real-time monitoring of the orientational relaxation towards isotropy. Our ratiometric analysis of the transverse electric and magnetic polarized fluorescence components allows for a distance-independent estimation of the rotational friction coefficient. Applying this method to covalently bound neutravidin, we observe a rotational friction coefficient (about 5.8E-18 J s) significantly higher than in bulk solutions, highlighting the impact of surface anchoring and molecular crowding. The proposed approach provides a robust, high-sensitivity platform for resolving biomolecular dynamics in complex interfacial environments.
Authors: Francesco Michelotti, Elisabetta Sepe, Agostino Occhicone, Norbert Danz, Alberto Sinibaldi
Citations: N/A
creditcorporate bond liquidity
ARXIV · 2026 · arXiv
This paper presents a method for forecasting limit order book durations using a self-exciting flexible residual point process. High-frequency events in modern exchanges exhibit heavy-tailed interarrival times, posing a significant challenge for accurate prediction. The proposed approach incorporates the empirical distributional features of interarrival times while preserving the self-exciting and decay structure. This work also examines the stochastic stability of the process, which can be interpreted as a general state-space Markov chain. Under suitable conditions, the process is irreducible, aperiodic, positive Harris recurrent, and has a stationary distribution. An empirical study demonstrates that the model achieves strong predictive performance compared with several alternative approaches when forecasting durations in ultra-high-frequency trading data.
Authors: Kyungsub Lee
Citations: N/A
high frequency tradingq-fin
ARXIV · 2026 · arXiv
We address the problem of executing large client orders in continuous double-auction markets under time and liquidity constraints. We propose a model predictive control (MPC) framework that balances three competing objectives: order completion, market impact, and opportunity cost. Our algorithm is guided by a trading schedule (such as time-weighted average price or volume-weighted average price) but allows for deviations to reduce the expected execution cost, with due regard to risk. Our MPC algorithm executes the order progressively, and at each decision step it solves a fast quadratic program that trades off expected transaction cost against schedule deviation, while incorporating a residual cost term derived from a simple base policy. Approximate schedule adherence is maintained through explicit bounds, while variance constraints on deviation provide direct risk control. The resulting system is modular, data-driven, and suitable for deployment in production trading infrastructure. Using six months of NASDAQ 'level 3' data and simulated orders, we show that our MPC approach reduces schedule shortfall by approximately 40-50% relative to spread-crossing benchmarks and achieves significant reductions in slippage. Moreover, augmenting the base policy with predictive price information further enhances performance, highlighting the framework's flexibility for integration with forecasting components.
Authors: Thomas P. McAuliffe, Samuel Liew, Yuchao Li, Andrey Ushenin, Chihang Wang, Alexandros Tasos, Jack Pearce, Dimitris Tasoulis
Citations: N/A
algorithmic executionq-fin
ARXIV · 2026 · arXiv
We propose Process-Aware Policy Optimization (PAPO), a method that integrates process-level evaluation into Group Relative Policy Optimization (GRPO) through decoupled advantage normalization, to address two limitations of existing reward designs. Outcome reward models (ORM) evaluate only final-answer correctness, treating all correct responses identically regardless of reasoning quality, and gradually lose the advantage signal as groups become uniformly correct. Process reward models (PRM) offer richer supervision, but directly using PRM scores causes reward hacking, where models exploit verbosity to inflate scores while accuracy collapses. PAPO resolves both by composing the advantage from an outcome component Aout, derived from ORM and normalized over all responses, and a process component Aproc, derived from a rubric-based PRM and normalized exclusively among correct responses. This decoupled design ensures that Aout anchors training on correctness while Aproc differentiates reasoning quality without distorting the outcome signal. Experiments across multiple model scales and six benchmarks demonstrate that PAPO consistently outperforms ORM, reaching 51.3% vs.\ 46.3% on OlympiadBench while continuing to improve as ORM plateaus and declines.
Authors: Zelin Tan, Zhouliang Yu, Bohan Lin, Zijie Geng, Hejia Geng, Yudong Zhang, Mulei Zhang, Yang Chen
Citations: N/A
macromonetary policy asset pricing
ARXIV · 2026 · arXiv
This paper develops an autonomous framework for systematic factor investing via agentic AI. Rather than relying on sequential manual prompts, our approach operationalizes the model as a self-directed engine that endogenously formulates interpretable trading signals. To mitigate data snooping biases, this closed-loop system imposes strict empirical discipline through out-of-sample validation and economic rationale requirements. Applying this methodology to the U.S. equity market, we document that long-short portfolios formed on the simple linear combination of signals deliver an annualized Sharpe ratio of 3.11 and a return of 59.53%. Finally, our empirics demonstrate that self-evolving AI offers a scalable and interpretable paradigm.
Authors: Allen Yikuan Huang, Zheqi Fan
Citations: N/A
systematic factor investingq-fin
ARXIV · 2026 · arXiv
Generating synthetic financial time series that preserve the statistical properties of real market data is essential for stress testing, risk model validation, and scenario design. Existing approaches struggle to simultaneously reproduce heavy-tailed distributions, negligible linear autocorrelation, and persistent volatility clustering. We developed a hybrid hidden Markov framework that discretized excess growth rates into Laplace quantile-defined states and augmented regime switching with a Poisson jump-duration mechanism to enforce realistic tail-state dwell times. Parameters were estimated by direct transition counting, bypassing the Baum-Welch EM algorithm and scaling to a 424-asset pipeline. Applied to ten years of daily equity data, the framework achieved high distributional pass rates both in-sample and out-of-sample while partially reproducing the volatility clustering that standard regime-switching models miss. No single model was best at everything: GARCH(1,1) better reproduced volatility clustering but failed distributional tests, while the standard HMM without jumps passed more distributional tests but could not generate volatility clustering. The proposed framework delivered the most balanced performance overall. For multi-asset generation, copula-based dependence models that preserved each asset's marginal HMM distribution substantially outperformed a Single-Index Model factor baseline on both per-asset distributional accuracy and correlation reproduction.
Authors: Abdulrahman Alswaidan, Jeffrey D. Varner
Citations: N/A
creditcredit default swap indexdistressed debt credit marketsmacromacro financemonetary policy asset pricing
ARXIV · 2026 · arXiv
We present a large scale benchmark of modern deep learning architectures for a financial time series prediction and position sizing task, with a primary focus on Sharpe ratio optimization. Evaluating linear models, recurrent networks, transformer based architectures, state space models, and recent sequence representation approaches, we assess out of sample performance on a daily futures dataset spanning commodities, equity indices, bonds, and FX spanning 2010 to 2025. Our evaluation goes beyond average returns and includes statistical significance, downside and tail risk measures, breakeven transaction cost analysis, robustness to random seed selection, and computational efficiency. We find that models explicitly designed to learn rich temporal representations consistently outperform linear benchmarks and generic deep learning models, which often lead the ranking in standard time series benchmarks. Hybrid models such as VSN with LSTM, a combination of Variable Selection Networks (VSN) and LSTMs, achieves the highest overall Sharpe ratio, while VSN with xLSTM and LSTM with PatchTST exhibit superior downside adjusted characteristics. xLSTM demonstrates the largest breakeven transaction cost buffer, indicating improved robustness to trading frictions.
Authors: Adir Saly-Kaufmann, Kieran Wood, Jan Peter-Calliess, Stefan Zohren
Citations: N/A
transaction cost analysisq-fin
ARXIV · 2026 · arXiv
The Martian brain terrain (MBT), characterized by its unique brain-like morphology, is a potential geological archive for finding hints of paleoclimatic conditions during its formation period. The morphological similarity of MBT to self-organized patterned ground on Earth suggests a shared formation mechanism. However, the lack of quantitative descriptions and robust physical modeling of self-organized stone transport jointly limits the study of the thermal and aqueous conditions governing MBT's formation. Here we established a specialized quantitative system for extracting the morphological features of MBT, taking a typical region located in the northern Arabia Terra as an example, and then employed a numerical model to investigate its formation mechanisms. Our simulation results accurately replicate the observed morphology of MBT, matching its key geometric metrics with deviations <15%. Crucially, however, we find that the self-organized transport can solely produce relief <0.5 m, insufficient to explain the formation of MBT with average relief of 3.29 \pm 0.65 m. We attribute this discrepancy to sculpting driven by late-stage sublimation, constraining cumulative subsurface ice loss in this region to ~3 meters over the past ~3 Ma. These findings demonstrate that MBT's formation is a multi-stage process: initial patterning driven by freeze-thaw cycles implying liquid water followed by vertical sculpting via sublimation requiring a dry environment. This evolution provides physical evidence for the transition of the ancient Martian climate from a wetter period to a colder hyper-arid state.
Authors: Shenyi Zhang, Lei Zhang, Yutian Ke, Jinhai Zhang
Citations: N/A
creditcorporate bond liquidity
ARXIV · 2026 · arXiv
High-Frequency trading (HFT) environments are characterised by large volumes of limit order book (LOB) data, which is notoriously noisy and non-linear. Alpha decay represents a significant challenge, with traditional models such as DeepLOB losing predictive power as the time horizon (k) increases. In this paper, using data from the FI-2010 dataset, we introduce Temporal Kolmogorov-Arnold Networks (T-KAN) to replace the fixed, linear weights of standard LSTMs with learnable B-spline activation functions. This allows the model to learn the 'shape' of market signals as opposed to just their magnitude. This resulted in a 19.1% relative improvement in the F1-score at the k = 100 horizon. The efficacy of T-KAN networks cannot be understated, producing a 132.48% return compared to the -82.76% DeepLOB drawdown under 1.0 bps transaction costs. In addition to this, the T-KAN model proves quite interpretable, with the 'dead-zones' being clearly visible in the splines. The T-KAN architecture is also uniquely optimized for low-latency FPGA implementation via High level Synthesis (HLS). The code for the experiments in this project can be found at https://github.com/AhmadMak/Temporal-Kolmogorov-Arnold-Networks-T-KAN-for-High-Frequency-Limit-Order-Book-Forecasting.
Authors: Ahmad Makinde
Citations: N/A
high frequency tradingq-fin
ARXIV · 2025 · arXiv
Drifts of asset returns are notoriously difficult to model accurately and, yet, trading strategies obtained from portfolio optimization are very sensitive to them. To mitigate this well-known phenomenon we study robust growth-optimization in a high-dimensional incomplete market under drift uncertainty of the asset price process $X$, under an additional ergodicity assumption, which constrains but does not fully specify the drift in general. The class of admissible models allows $X$ to depend on a multivariate stochastic factor $Y$ and fixes (a) their joint volatility structure, (b) their long-term joint ergodic density and (c) the dynamics of the stochastic factor process $Y$. A principal motivation of this framework comes from pairs trading, where $X$ is the spread process and models with the above characteristics are commonplace. Our main results determine the robust optimal growth rate, construct a worst-case admissible model and characterize the robust growth-optimal strategy via a solution to a certain partial differential equation (PDE). We demonstrate that utilizing the stochastic factor leads to improvement in robust growth complementing the conclusions of the previous study by Itkin et. al. (arXiv:2211.15628 [q-fin.MF], forthcoming in $\textit{Finance and Stochastics}$), which additionally robustified the dynamics of the stochastic factor leading to $Y$-independent optimal strategies. Our analysis leads to new financial insights, quantifying the improvement in growth the investor can achieve by optimally incorporating stochastic factors into their trading decisions. We illustrate our theoretical results on several numerical examples including an application to pairs trading.
Authors: Balint Binkert, David Itkin, Paul Mangers Bastian, Josef Teichmann
Citations: N/A
pairs tradingq-fin
ARXIV · 2025 · arXiv
This follow-up article analyzes the impact of foreign exchange option interpolation on the vanilla option implied volatilities. In particular different exact interpolations of broker quotes may lead to different implied volatilities at the 10$Δ$ and 25$Δ$ Puts and Calls.
Authors: Jherek Healy
Citations: N/A
options implied volatilityq-fin
ARXIV · 2025 · arXiv
The integration of Deep Reinforcement Learning (DRL) and Evolutionary Computation (EC) is frequently hypothesized to be the "Holy Grail" of algorithmic trading, promising systems that adapt autonomously to non-stationary market regimes. This paper presents a rigorous post-mortem analysis of "Galaxy Empire," a hybrid framework coupling LSTM/Transformer-based perception with a genetic "Time-is-Life" survival mechanism. Deploying a population of 500 autonomous agents in a high-frequency cryptocurrency environment, we observed a catastrophic divergence between training metrics (Validation APY $>300\%$) and live performance (Capital Decay $>70\%$). We deconstruct this failure through a multi-disciplinary lens, identifying three critical failure modes: the overfitting of \textit{Aleatoric Uncertainty} in low-entropy time-series, the \textit{Survivor Bias} inherent in evolutionary selection under high variance, and the mathematical impossibility of overcoming microstructure friction without order-flow data. Our findings provide empirical evidence that increasing model complexity in the absence of information asymmetry exacerbates systemic fragility.
Authors: Yijia Chen
Citations: N/A
high frequency tradingq-fin
ARXIV · 2025 · arXiv
Modern high-frequency trading (HFT) environments are characterized by sudden price spikes that present both risk and opportunity, but conventional financial models often fail to capture the required fine temporal structure. Spiking Neural Networks (SNNs) offer a biologically inspired framework well-suited to these challenges due to their natural ability to process discrete events and preserve millisecond-scale timing. This work investigates the application of SNNs to high-frequency price-spike forecasting, enhancing performance via robust hyperparameter tuning with Bayesian Optimization (BO). This work converts high-frequency stock data into spike trains and evaluates three architectures: an established unsupervised STDP-trained SNN, a novel SNN with explicit inhibitory competition, and a supervised backpropagation network. BO was driven by a novel objective, Penalized Spike Accuracy (PSA), designed to ensure a network's predicted price spike rate aligns with the empirical rate of price events. Simulated trading demonstrated that models optimized with PSA consistently outperformed their Spike Accuracy (SA)-tuned counterparts and baselines. Specifically, the extended SNN model with PSA achieved the highest cumulative return (76.8%) in simple backtesting, significantly surpassing the supervised alternative (42.54% return). These results validate the potential of spiking networks, when robustly tuned with task-specific objectives, for effective price spike forecasting in HFT.
Authors: Brian Ezinwoke, Oliver Rhodes
Citations: N/A
high frequency tradingq-fin
ARXIV · 2025 · arXiv
Markets efficiency implies that the stock returns are intrinsically unpredictable, a property that makes markets comparable to random number generators. We present a novel methodology to investigate ultra-high frequency financial data and to evaluate the extent to which tick by tick returns resemble random sequences. We extend the analysis of ultra high-frequency stock market data by applying comprehensive sets of randomness tests, beyond the usual reliance on serial correlation or entropy measures. Our purpose is to extensively analyze the randomness of these data using statistical tests from standard batteries that evaluate different aspects of randomness. We illustrate the effect of time aggregation in transforming highly correlated high-frequency trade data to random streams. More specifically, we use many of the tests in the NIST Statistical Test Suite and in the TestU01 battery (in particular the Rabbit and Alphabit sub-batteries), to prove that the degree of randomness of financial tick data increases together with the increase of the aggregation level in transaction time. Additionally, the comprehensive nature of our tests also uncovers novel patterns, such as non-monotonic behaviors in predictability for certain assets. This study demonstrates a model-free approach for both assessing randomness in financial time series and generating pseudo-random sequences from them, with potential relevance in several applications.
Authors: Silvia Onofri, Andrey Shternshis, Stefano Marmi
Citations: N/A
high frequency tradingq-fin
ARXIV · 2025 · arXiv
We study a systematic approach to a popular Statistical Arbitrage technique: Pairs Trading. Instead of relying on two highly correlated assets, we replace the second asset with a replication of the first using risk factor representations. These factors are obtained through Principal Components Analysis (PCA), exchange traded funds (ETFs), and, as our main contribution, Long Short Term Memory networks (LSTMs). Residuals between the main asset and its replication are examined for mean reversion properties, and trading signals are generated for sufficiently fast mean reverting portfolios. Beyond introducing a deep learning based replication method, we adapt the framework of Avellaneda and Lee (2008) to the Polish market. Accordingly, components of WIG20, mWIG40, and selected sector indices replace the original S&P500 universe, and market parameters such as the risk free rate and transaction costs are updated to reflect local conditions. We outline the full strategy pipeline: risk factor construction, residual modeling via the Ornstein Uhlenbeck process, and signal generation. Each replication technique is described together with its practical implementation. Strategy performance is evaluated over two periods: 2017-2019 and the recessive year 2020. All methods yield profits in 2017-2019, with PCA achieving roughly 20 percent cumulative return and an annualized Sharpe ratio of up to 2.63. Despite multiple adaptations, our conclusions remain consistent with those of the original paper. During the COVID-19 recession, only the ETF based approach remains profitable (about 5 percent annual return), while PCA and LSTM methods underperform. LSTM results, although negative, are promising and indicate potential for future optimization.
Authors: Marek Adamczyk, Michał Dąbrowski
Citations: N/A
statistical arbitrageq-finpairs trading
ARXIV · 2025 · arXiv
Reinforcement Learning (RL) applied to financial problems has been the subject of a lively area of research. The use of RL for optimal trading strategies that exploit latent information in the market is, to the best of our knowledge, not widely tackled. In this paper we study an optimal trading problem, where a trading signal follows an Ornstein-Uhlenbeck process with regime-switching dynamics. We employ a blend of RL and Recurrent Neural Networks (RNN) in order to make the most at extracting underlying information from the trading signal with latent parameters. The latent parameters driving mean reversion, speed, and volatility are filtered from observations of the signal, and trading strategies are derived via RL. To address this problem, we propose three Deep Deterministic Policy Gradient (DDPG)-based algorithms that integrate Gated Recurrent Unit (GRU) networks to capture temporal dependencies in the signal. The first, a one -step approach (hid-DDPG), directly encodes hidden states from the GRU into the RL trader. The second and third are two-step methods: one (prob-DDPG) makes use of posterior regime probability estimates, while the other (reg-DDPG) relies on forecasts of the next signal value. Through extensive simulations with increasingly complex Markovian regime dynamics for the trading signal's parameters, as well as an empirical application to equity pair trading, we find that prob-DDPG achieves superior cumulative rewards and exhibits more interpretable strategies. By contrast, reg-DDPG provides limited benefits, while hid-DDPG offers intermediate performance with less interpretable strategies. Our results show that the quality and structure of the information supplied to the agent are crucial: embedding probabilistic insights into latent regimes substantially improves both profitability and robustness of reinforcement learning-based trading strategies.
Authors: Andrea Macrì, Sebastian Jaimungal, Fabrizio Lillo
Citations: N/A
pairs tradingq-fin
ARXIV · 2025 · arXiv
Statistical arbitrage exploits temporal price differences between similar assets. We develop a framework to jointly identify similar assets through factors, identify mispricing and form a trading policy that maximizes risk-adjusted performance after trading costs. Our Attention Factors are conditional latent factors that are the most useful for arbitrage trading. They are learned from firm characteristic embeddings that allow for complex interactions. We identify time-series signals from the residual portfolios of our factors with a general sequence model. Estimating factors and the arbitrage trading strategy jointly is crucial to maximize profitability after trading costs. In a comprehensive empirical study we show that our Attention Factor model achieves an out-of-sample Sharpe ratio above 4 on the largest U.S. equities over a 24-year period. Our one-step solution yields an unprecedented Sharpe ratio of 2.3 net of transaction costs. We show that weak factors are important for arbitrage trading.
Authors: Elliot L. Epstein, Rose Wang, Jaewon Choi, Markus Pelger
Citations: N/A
statistical arbitrageq-fin
ARXIV · 2025 · arXiv
In the LIBOR era, banks routinely tied revolving credit facilities to credit-sensitive benchmarks. This study assesses the Across-the-Curve Credit Spread Index (AXI) -- a transparent, transaction-based measure of wholesale bank funding costs -- as a complement to SOFR, summarizing its behavior, construction, and loan-pricing implications. AXI aggregates observable unsecured funding transactions across short- and long-term maturities to produce a daily credit spread that is IOSCO-aligned and operationally compatible with SOFR-based infrastructure. The Financial Conditions Credit Spread Index (FXI) is a broader market companion to AXI and serves as its fallback. FXI co-moves closely with AXI in normal times; under stress, the correlation of daily changes exceeds 0.9 for economy-wide shocks and remains strong in bank-specific stress, around 0.8 during the Silicon Valley Bank episode. Empirically, AXI is strongly correlated with standard credit-spread measures and market-stress indicators and is inversely related to financial-sector performance. SOFR+AXI exhibits correlations with macroeconomic variables with the signs and magnitudes expected of a credit-sensitive rate. In loan-pricing applications, SOFR+AXI reduces funding risk and can support spread discounts of up to 65 basis points without lowering risk-adjusted returns. In stress scenarios, banks relying on SOFR-only pricing can fail to recover as much as 15 basis points on revolving credit lines over as little as three months. Taken together, AXI restores the credit sensitivity lost in the USD LIBOR transition while avoiding reliance on thin short-term markets, delivering significant economic value.
Authors: Viktor Tsyrennikov
Citations: N/A
credit spreadsq-fin
ARXIV · 2025 · arXiv
We propose a two-step graph learning approach for foreign exchange statistical arbitrages (FXSAs), addressing two key gaps in prior studies: the absence of graph-learning methods for foreign exchange rate prediction (FXRP) that leverage multi-currency and currency-interest rate relationships, and the disregard of the time lag between price observation and trade execution. In the first step, to capture complex multi-currency and currency-interest rate relationships, we formulate FXRP as an edge-level regression problem on a discrete-time spatiotemporal graph. This graph consists of currencies as nodes and exchanges as edges, with interest rates and foreign exchange rates serving as node and edge features, respectively. We then introduce a graph-learning method that leverages the spatiotemporal graph to address the FXRP problem. In the second step, we present a stochastic optimization problem to exploit FXSAs while accounting for the observation-execution time lag. To address this problem, we propose a graph-learning method that enforces constraints through projection and ReLU, maximizes risk-adjusted return by leveraging a graph with exchanges as nodes and influence relationships as edges, and utilizes the predictions from the FXRP method for the constraint parameters and node features. Moreover, we prove that our FXSA method satisfies empirical arbitrage constraints. The experimental results demonstrate that our FXRP method yields statistically significant improvements in mean squared error, and that the FXSA method achieves a 61.89% higher information ratio and a 45.51% higher Sortino ratio than a benchmark. Our approach provides a novel perspective on FXRP and FXSA within the context of graph learning.
Authors: Yoonsik Hong, Diego Klabjan
Citations: N/A
statistical arbitrageq-fin
ARXIV · 2025 · arXiv
This paper examines systematic put-writing strategies applied to S&P 500 Index options, with a focus on position sizing as a key determinant of long-term performance. Despite the well-documented volatility risk premium, where implied volatility exceeds realized volatility, the practical implementation of short-dated volatility-selling strategies remains underdeveloped in the literature. This study evaluates three position sizing approaches: the Kelly criterion, VIX-based volatility regime scaling, and a novel hybrid method combining both. Using SPXW options with expirations from 0 to 5 days, the analysis explores a broad design space, including moneyness levels, volatility estimators, and memory horizons. Results show that ultra-short-dated, far out-of-the-money options deliver superior risk-adjusted returns. The hybrid sizing method consistently balances return generation with robust drawdown control, particularly under low-volatility conditions such as those seen in 2024. The study offers new insights into volatility harvesting, introducing a dynamic sizing framework that adapts to shifting market regimes. It also contributes practical guidance for constructing short-dated option strategies that are robust across market environments. These findings have direct applications for institutional investors seeking to enhance portfolio efficiency through systematic exposure to volatility premia.
Authors: Maciej Wysocki
Citations: N/A
volatility risk premiumq-fin
ARXIV · 2025 · arXiv
Accurate volatility forecasts are vital in modern finance for risk management, portfolio allocation, and strategic decision-making. However, existing methods face key limitations. Fully multivariate models, while comprehensive, are computationally infeasible for realistic portfolios. Factor models, though efficient, primarily use static factor loadings, failing to capture evolving volatility co-movements when they are most critical. To address these limitations, we propose a novel, model-agnostic Factor-Augmented Volatility Forecast framework. Our approach employs a time-varying factor model to extract a compact set of dynamic, cross-sectional factors from realized volatilities with minimal computational cost. These factors are then integrated into both statistical and AI-based forecasting models, enabling a unified system that jointly models asset-specific dynamics and evolving market-wide co-movements. Our framework demonstrates strong performance across two prominent asset classes-large-cap U.S. technology equities and major cryptocurrencies-over both short-term (1-day) and medium-term (7-day) horizons. Using a suite of linear and non-linear AI-driven models, we consistently observe substantial improvements in predictive accuracy and economic value. Notably, a practical pairs-trading strategy built on our forecasts delivers superior risk-adjusted returns and profitability, particularly under adverse market conditions.
Authors: Duo Zhang, Jiayu Li, Junyi Mo, Elynn Chen
Citations: N/A
pairs tradingq-fin
ARXIV · 2025 · arXiv
We explore the interplay between sovereign debt default/renegotiation and environmental factors (e.g., pollution from land use, natural resource exploitation). Pollution contributes to the likelihood of natural disasters and influences economic growth rates. The country can default on its debt at any time while also deciding whether to invest in pollution abatement. The framework provides insights into the credit spreads of sovereign bonds and explains the observed relationship between bond spread and a country's climate vulnerability. Through calibration for developing and low-income countries, we demonstrate that there is limited incentive for these countries to address climate risk, and the sensitivity of bond spreads to climate vulnerability remains modest. Climate risk does not play a relevant role on the decision to default on sovereign debt. Financial support for climate abatement expenditures can effectively foster climate adaptation actions, instead renegotiation conditional upon pollution abatement does not produce any effect.
Authors: Emilio Barucci, Daniele Marazzina, Aldo Nassigh
Citations: N/A
credit spreadsq-fin
SEMANTIC SCHOLAR · 2025 · The Journal of Financial Data Science
Factor models are essential tools for understanding asset returns. Statistical factor models such as principal component analysis (PCA) and autoencoders have been widely used to reduce the high-dimensional panels of returns into a lower-dimensional latent space. Although effective at retaining much of the original variance, these models often lack inherent economic interpretation and rely solely on historical data, failing to incorporate contextual features such as asset characteristics into factor construction. Consequently, ad hoc analyses are often required to assign real-world meaning to latent factors. To address these limitations, this article introduces a novel graph factor model (GFM) that integrates domain-informed sparsity, explicitly connecting factors to financially validated features to enable interpretable and robust factor extraction. Extensive experiments on modeling corporate spread returns demonstrate that the GFM captures more variance, is more robust to missing data, and provides clearer economic insights than PCA, autoencoders, and instrumented PCA. By bridging the gap between statistical performance and economic interpretability, this new framework supports tasks such as performance attribution and offers valuable insights for portfolio management.
Authors: Ashraf Ghiye, Baptiste Barreau, Laurent Carlier, M. Vazirgiannis
Citations: 0
creditcredit spread decomposition
ARXIV · 2024 · arXiv
This paper introduces a new algorithmic execution model that integrates interbank limit and market orders with internal liquidity generated through market making. Based on the Cartea et al.\cite{cartea2015algorithmic} framework, we incorporate market impact in interbank orders while excluding it for internal market-making transactions. Our model aims to optimize the balance between interbank and internal liquidity, reducing market impact and improving execution efficiency.
Authors: Yusuke Morimoto
Citations: N/A
algorithmic executionq-fin
ARXIV · 2024 · arXiv
We conduct a preliminary analysis of a pairs trading strategy using the Ornstein-Uhlenbeck (OU) process to model stock price spreads. We compare this approach to a naive pairs trading strategy that uses a rolling window to calculate mean and standard deviation parameters. Our findings suggest that the OU model captures signals and trends effectively but underperforms the naive model on a risk-return basis, likely due to non-stationary pairs and parameter tuning limitations.
Authors: Jirat Suchato, Sean Wiryadi, Danran Chen, Ava Zhao, Michael Yue
Citations: N/A
pairs tradingq-fin
ARXIV · 2024 · arXiv
The financial industry has undergone a significant transition from the London Interbank Offered Rates (LIBORs) to Risk Free Rates (RFRs) such as, e.g., the Secured Overnight Financing Rate (SOFR) in the U.S. and the Cash Rate (AONIA) in Australia, as primary benchmark rates for borrowing costs. The paper examines the pricing and hedging method for financial products in a cross-currency framework with the special emphasis on the Compound SOFR vs Average AONIA cross-currency basis swap (CCBS) where both reference rates are backward-looking and the swap is collateralized. While the SOFR and AONIA are used as particular instances of RFRs in a cross-currency basis swap, the proposed approach is able to handle backward-looking rates for any two currencies. We give explicit pricing and hedging results for a constant notional cross-currency basis swap with either domestic or foreign collateralization using interest rate futures and currency futures as hedging instruments within an arbitrage-free cross-currency multi-curve setting.
Authors: Yining Ding, Ruyi Liu, Marek Rutkowski
Citations: N/A
cross currency basisq-fin
ARXIV · 2024 · arXiv
This paper introduces a novel stochastic model for credit spreads. The stochastic approach leverages the diffusion of default intensities via a CIR++ model and is formulated within a risk-neutral probability space. Our research primarily addresses two gaps in the literature. The first is the lack of credit spread models founded on a stochastic basis that enables continuous modeling, as many existing models rely on factorial assumptions. The second is the limited availability of models that directly yield a term structure of credit spreads. An intermediate result of our model is the provision of a term structure for the prices of defaultable bonds. We present the model alongside an innovative, practical, and conservative calibration approach that minimizes the error between historical and theoretical volatilities of default intensities. We demonstrate the robustness of both the model and its calibration process by comparing its behavior to historical credit spread values. Our findings indicate that the model not only produces realistic credit spread term structure curves but also exhibits consistent diffusion over time. Additionally, the model accurately fits the initial term structure of implied survival probabilities and provides an analytical expression for the credit spread of any given maturity at any future time.
Authors: Mohamed Ben Alaya, Ahmed Kebaier, Djibril Sarr
Citations: N/A
credit spreadsq-fin
ARXIV · 2024 · arXiv
In this study, we constructed daily high-frequency sentiment data and used the VAR method to attempt to predict the next day's implied volatility surface. We utilized 630,000 text data entries from the East Money Stock Forum from 2014 to 2023 and employed deep learning methods such as BERT and LSTM to build daily market sentiment indicators. By applying FFT and EMD methods for sentiment decomposition, we found that high-frequency sentiment had a stronger correlation with at-the-money (ATM) options' implied volatility, while low-frequency sentiment was more strongly correlated with deep out-of-the-money (DOTM) options' implied volatility. Further analysis revealed that the shape of the implied volatility surface contains richer market sentiment information beyond just market panic. We demonstrated that incorporating this sentiment information can improve the accuracy of implied volatility surface predictions.
Authors: Jiahao Weng, Yan Xie
Citations: N/A
options implied volatilityq-fin
ARXIV · 2024 · arXiv
This paper introduces a new risk-on risk-off strategy for the stock market, which combines a financial stress indicator with a sentiment analysis done by ChatGPT reading and interpreting Bloomberg daily market summaries. Forecasts of market stress derived from volatility and credit spreads are enhanced when combined with the financial news sentiment derived from GPT-4. As a result, the strategy shows improved performance, evidenced by higher Sharpe ratio and reduced maximum drawdowns. The improved performance is consistent across the NASDAQ, the S&P 500 and the six major equity markets, indicating that the method generalises across equities markets.
Authors: Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez, Thomas Jacquot
Citations: N/A
credit spreadsq-fin
ARXIV · 2024 · arXiv
A growing number of contributions in the literature have identified a puzzle in the European carbon allowance (EUA) market. Specifically, a persistent cost-of-carry spread (C-spread) over the risk-free rate has been observed. We are the first to explain the anomalous C-spread with the credit spread of the corporates involved in the emission trading scheme. We obtain statistical evidence that the C-spread is cointegrated with both this credit spread and the risk-free interest rate. This finding has a relevant policy implication: the most effective solution to solve the market anomaly is including the EUA in the list of European Central Bank eligible collateral for refinancing operations. This change in the ECB monetary policy operations would greatly benefit the carbon market and the EU green transition.
Authors: Michele Azzone, Roberto Baviera, Pietro Manzoni
Citations: N/A
credit spreadsq-fin
SEMANTIC SCHOLAR · 2024 · Financial Innovation
Financing sources for urban construction have garnered significant attention globally. Among various financing methods, the urban construction investment bond (UCIB) is unique to China. The UCIB credit spread, which represents the compensation for credit risk, has become a focal point for researchers. However, owing to shortcomings of previous approaches, few scholars have accurately assessed the impact of implicit government guarantees on credit spreads. This study introduces an innovative approach that uses orthogonal decomposition to extract proprietary information from credit ratings, reflecting implicit government guarantees. After accounting for bond factors, local government financing vehicle factors, and macroeconomic conditions, the implicit government guarantee substantially reduces the UCIB's credit spread. This conclusion remains robust when controlling for investor attention, regional factors, or duration.
Authors: Rongda Chen, Han Li, Xuhui Tang, Chenglu Jin, Shuonan Zhang, Xinyu Zhang
Citations: 1
creditcredit spread decomposition
ARXIV · 2023 · arXiv
We propose a new model for the forecasting of both the implied volatility surfaces and the underlying asset price. In the spirit of Guyon and Lekeufack (2023) who are interested in the dependence of volatility indices (e.g. the VIX) on the paths of the associated equity indices (e.g. the S\&P 500), we first study how vanilla options implied volatility can be predicted using the past trajectory of the underlying asset price. Our empirical study reveals that a large part of the movements of the at-the-money-forward implied volatility for up to two years time-to-maturities can be explained using the past returns and their squares. Moreover, we show that this feedback effect gets weaker when the time-to-maturity increases. Building on this new stylized fact, we fit to historical data a parsimonious version of the SSVI parameterization (Gatheral and Jacquier, 2014) of the implied volatility surface relying on only four parameters and show that the two parameters ruling the at-the-money-forward implied volatility as a function of the time-to-maturity exhibit a path-dependent behavior with respect to the underlying asset price. Finally, we propose a model for the joint dynamics of the implied volatility surface and the underlying asset price. The latter is modelled using a variant of the path-dependent volatility model of Guyon and Lekeufack and the former is obtained by adding a feedback effect of the underlying asset price onto the two parameters ruling the at-the-money-forward implied volatility in the parsimonious SSVI parameterization and by specifying Ornstein-Uhlenbeck processes for the residuals of these two parameters and Jacobi processes for the two other parameters. Thanks to this model, we are able to simulate highly realistic paths of implied volatility surfaces that are free from static arbitrage.
Authors: Hervé Andrès, Alexandre Boumezoued, Benjamin Jourdain
Citations: N/A
options implied volatilityq-fin
ARXIV · 2023 · arXiv
We provide a general HJM framework for forward contracts written on abstract market indices with arbitrary fixing and payment adjustments, and featuring collateralization in any currency denominations. In view of this, we first provide a thorough study of cross-currency markets in the presence of collateral and incompleteness. Then we give a general treatment of collateral dislocations by describing the instantaneous cross-currency basis spreads by means of HJM models, for which we derive appropriate drift conditions. The framework obtained allows us to simultaneously cover forward-looking risky IBOR rates, such as EURIBOR, and backward-looking rates based on overnight rates, such as SOFR. Due to the discrepancies in market conventions of different currency areas created by the benchmark transition, this is pivotal for describing portfolios of interest-rate products that are denominated in multiple currencies. As an example of contract simultaneously depending on all the risk factors that we describe within our framework, we treat cross-currency swaps using our proposed abstract indices.
Authors: Alessandro Gnoatto, Silvia Lavagnini
Citations: N/A
cross currency basisq-fin
ARXIV · 2023 · arXiv
In this paper, we consider a generic interest rate market in the presence of roll-over risk, which generates spreads in spot/forward term rates. We do not require classical absence of arbitrage and rely instead on a minimal market viability assumption, which enables us to work in the context of the benchmark approach. In a Markovian setting, we extend the control theoretic approach of Gombani & Runggaldier (2013) and derive representations of spot/forward spreads as value functions of suitable stochastic optimal control problems, formulated under the real-world probability and with power-type objective functionals. We determine endogenously the funding-liquidity spread by relating it to the risk-sensitive optimization problem of a representative investor.
Authors: Claudio Fontana, Simone Pavarana, Wolfgang J. Runggaldier
Citations: N/A
funding liquidityq-fin
OPENALEX · 2023 · ACM Transactions on Intelligent Systems and Technology
Quantitative trading (QT) , which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement learning (RL) has garnered significant interest in many domains such as robotics and video games, owing to its outstanding ability on solving complex sequential decision making problems. RL’s impact is pervasive, recently demonstrating its ability to conquer many challenging QT tasks. It is a flourishing research direction to explore RL techniques’ potential on QT tasks. This paper aims at providing a comprehensive survey of research efforts on RL-based methods for QT tasks. More concretely, we devise a taxonomy of RL-based QT models, along with a comprehensive summary of the state of the art. Finally, we discuss current challenges and propose future research directions in this exciting field.
Authors: Shuo Sun, Rundong Wang, Bo An
Citations: 65
Stock Market Forecasting MethodsData Stream Mining TechniquesReinforcement Learning in Robotics
ARXIV · 2022 · arXiv
We propose a non-linear observation-driven version of the Hasbrouck (1991) model for dynamically estimating trades' market impact and information content. We find that market impact displays an intraday pattern superimposed with large fluctuations. Some of them are exogenous, and, as an example, we investigate market impact dynamics around FOMC announcements. Contrary to Hasbrouck (1991), we find that the information content of trades depends on the local liquidity level and the recent history of prices and trades. Finally, we use the model to estimate the time-varying permanent impact parameter, which allows performing a dynamic transaction cost analysis.
Authors: F. Campigli, G. Bormetti, F. Lillo
Citations: N/A
transaction cost analysisq-fin
OPENALEX · 2022 · Review of Financial Studies
Abstract We identify fixed-income mutual funds as an important contributor to the unusually high selling pressure in liquid asset markets during the COVID-19 crisis. We show that mutual funds experienced pronounced investor outflows amplified by their liquidity transformation. In meeting redemptions, funds followed a pecking order by first selling their liquid assets, including Treasuries and high-quality corporate bonds, which generated the most concentrated selling pressure in these markets. Overall, the estimated price impact of mutual funds was sizable at a third of the increase in Treasury yields and a quarter of the increase in corporate bond yields during the COVID-19 crisis.
Authors: Yiming Ma, Kairong Xiao, Yao Zeng
Citations: 198
Financial Markets and Investment StrategiesBanking stability, regulation, efficiencyHousing Market and Economics
OPENALEX · 2022 · Review of Financial Studies
Abstract Two intermediary-based factors—a corporate bond dealer inventory measure and a broad intermediary distress measure—explain more than 40$\%$ of the puzzling common variation in credit spread changes beyond canonical structural factors. A simple intermediary-based model with partial market segmentation accounts for intermediary factors’ explanatory power and delivers three further implications with empirical support. First, whereas bond sorts on risk-related variables produce monotonic loading patterns on intermediary factors, non-risk-related sorts produce no pattern. Second, dealer inventory comoves with corporate-credit assets only, whereas intermediary distress comoves with both corporate-credit and non-corporate-credit assets. Third, dealers’ inventory responds to (instrumented) bond sales by institutional investors.
Authors: Zhiguo He, Paymon Khorrami, Zhaogang Song
Citations: 55
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyCorporate Finance and Governance
ARXIV · 2022 · arXiv
Applying historical data from the USD LIBOR transition period, we estimate a joint model for SOFR, Fed Funds, and Eurodollar futures rates as well as spot USD LIBOR and term repo rates. The framework endogenously models basis spreads between each of the benchmark rates and allows for the decomposition of spreads. Modelling the LIBOR-OIS spread as credit and funding-liquidity roll-over risk, we find that the spike in the LIBOR-OIS spread during the onset of COVID-19 was mainly due to credit risk, while on average credit and funding-liquidity risk contribute equally to the spread.
Authors: David Skovmand, Jacob Bjerre Skov
Citations: N/A
funding liquidityq-fin
ARXIV · 2021 · arXiv
We show that the Realized GARCH model yields close-form expression for both the Volatility Index (VIX) and the volatility risk premium (VRP). The Realized GARCH model is driven by two shocks, a return shock and a volatility shock, and these are natural state variables in the stochastic discount factor (SDF). The volatility shock endows the exponentially affine SDF with a compensation for volatility risk. This leads to dissimilar dynamic properties under the physical and risk-neutral measures that can explain time-variation in the VRP. In an empirical application with the S&P 500 returns, the VIX, and the VRP, we find that the Realized GARCH model significantly outperforms conventional GARCH models.
Authors: Peter Reinhard Hansen, Zhuo Huang, Chen Tong, Tianyi Wang
Citations: N/A
volatility risk premiumq-fin
ARXIV · 2021 · arXiv
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers the modeling of the liability liquidity risk (or funding liquidity), the second dimension is dedicated to the modeling of the asset liquidity risk (or market liquidity), whereas the third dimension considers the management of the asset-liability liquidity risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. In this third and last research paper focused on managing the asset-liability liquidity risk, we explore the ALM tools that can be put in place to control the liquidity gap. These ALM tools can be split into three categories: measurement tools, management tools and monitoring tools. In terms of measurement tools, we focus on the computation of the redemption coverage ratio (RCR), which is the central instrument of liquidity stress testing programs. We also study the redemption liquidation policy and the different implementation methodologies, and we show how reverse stress testing can be developed. In terms of liquidity management tools, we study the calibration of liquidity buffers, the pros and cons of special arrangements (redemption suspensions, gates, side pockets and in-kind redemptions) and the effectiveness of swing pricing. In terms of liquidity monitoring tools, we compare the macro- and micro-approaches of liquidity monitoring in order to identify the transmission channels of liquidity risk.
Authors: Thierry Roncalli
Citations: N/A
funding liquidityq-fin
OPENALEX · 2021 · The Lancet Neurology
BACKGROUND: Regularly updated data on stroke and its pathological types, including data on their incidence, prevalence, mortality, disability, risk factors, and epidemiological trends, are important for evidence-based stroke care planning and resource allocation. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) aims to provide a standardised and comprehensive measurement of these metrics at global, regional, and national levels. METHODS: We applied GBD 2019 analytical tools to calculate stroke incidence, prevalence, mortality, disability-adjusted life-years (DALYs), and the population attributable fraction (PAF) of DALYs (with corresponding 95% uncertainty intervals [UIs]) associated with 19 risk factors, for 204 countries and territories from 1990 to 2019. These estimates were provided for ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage, and all strokes combined, and stratified by sex, age group, and World Bank country income level. FINDINGS: In 2019, there were 12·2 million (95% UI 11·0-13·6) incident cases of stroke, 101 million (93·2-111) prevalent cases of stroke, 143 million (133-153) DALYs due to stroke, and 6·55 million (6·00-7·02) deaths from stroke. Globally, stroke remained the second-leading cause of death (11·6% [10·8-12·2] of total deaths) and the third-leading cause of death and disability combined (5·7% [5·1-6·2] of total DALYs) in 2019. From 1990 to 2019, the absolute number of incident strokes increased by 70·0% (67·0-73·0), prevalent strokes increased by 85·0% (83·0-88·0), deaths from stroke increased by 43·0% (31·0-55·0), and DALYs due to stroke increased by 32·0% (22·0-42·0). During the same period, age-standardised rates of stroke incidence decreased by 17·0% (15·0-18·0), mortality decreased by 36·0% (31·0-42·0), prevalence decreased by 6·0% (5·0-7·0), and DALYs decreased by 36·0% (31·0-42·0). However, among people younger than 70 years, prevalence rates increased by 22·0% (21·0-24·0) and incidence rates increased by 15·0% (12·0-18·0). In 2019, the age-standardised stroke-related mortality rate was 3·6 (3·5-3·8) times higher in the World Bank low-income group than in the World Bank high-income group, and the age-standardised stroke-related DALY rate was 3·7 (3·5-3·9) times higher in the low-income group than the high-income group. Ischaemic stroke constituted 62·4% of all incident strokes in 2019 (7·63 million [6·57-8·96]), while intracerebral haemorrhage constituted 27·9% (3·41 million [2·97-3·91]) and subarachnoid haemorrhage constituted 9·7% (1·18 million [1·01-1·39]). In 2019, the five leading risk factors for stroke were high systolic blood pressure (contributing to 79·6 million [67·7-90·8] DALYs or 55·5% [48·2-62·0] of total stroke DALYs), high body-mass index (34·9 million [22·3-48·6] DALYs or 24·3% [15·7-33·2]), high fasting plasma glucose (28·9 million [19·8-41·5] DALYs or 20·2% [13·8-29·1]), ambient particulate matter pollution (28·7 million [23·4-33·4] DALYs or 20·1% [16·6-23·0]), and smoking (25·3 million [22·6-28·2] DALYs or 17·6% [16·4-19·0]). INTERPRETATION: The annual number of strokes and deaths due to stroke increased substantially from 1990 to 2019, despite substantial reductions in age-standardised rates, particularly among people older than 70 years. The highest age-standardised stroke-related mortality and DALY rates were in the World Bank low-income group. The fastest-growing risk factor for stroke between 1990 and 2019 was high body-mass index. Without urgent implementation of effective primary prevention strategies, the stroke burden will probably continue to grow across the world, particularly in low-income countries. FUNDING: Bill & Melinda Gates Foundation.
OPENALEX · 2021
While institutional traders continue to implement quantitative (or algorithmic) trading, many independent traders have wondered if they can still challenge powerful industry professionals at their own game? The answer is "yes," and in Quantitative Trading, Dr. Ernest Chan, a respected independent trader and consultant, will show you how. Whether you're an independent "retail" trader looking to start your own quantitative trading business or an individual who aspires to work as a quantitative trader at a major financial institution, this practical guide contains the information you need to succeed.
Authors: Ernest P. Chan
Citations: 118
Big Data and Business IntelligenceBusiness Strategy and InnovationEconomic theories and models
ARXIV · 2021 · arXiv
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal of developing mathematical and statistical approaches, and providing appropriate answers. In this second article focused on asset liquidity risk modeling, we propose a market impact model to estimate transaction costs. After presenting a toy model that helps to understand the main concepts of asset liquidity, we consider a two-regime model, which is based on the power-law property of price impact. Then, we define several asset liquidity measures such as liquidity cost, liquidation ratio and shortfall or time to liquidation in order to assess the different dimensions of asset liquidity. Finally, we apply this asset liquidity framework to stocks and bonds and discuss the issues of calibrating the transaction cost model.
Authors: Thierry Roncalli, Amina Cherief, Fatma Karray-Meziou, Margaux Regnault
Citations: N/A
funding liquidityq-fin
ARXIV · 2021 · arXiv
This paper examines how shocks to currency volatilities predict exchange rates. Using option-implied volatilities, we construct a dynamic, directed network of volatility connections. Currencies that transmit more volatility shocks, which control for common correlation, earn lower excess returns. Buying the weakest and selling the strongest transmitters delivers high risk-adjusted performance, driven by spot exchange rate movements and not explained by standard factors. A general equilibrium model shows that volatility transmission related to idiosyncratic shocks proxies for priced country-specific risk. Assuming a monotonic amplification of domestic idiosyncratic risk, volatility transmission forecasts negatively future excess returns, consistent with the empirical evidence.
Authors: Mykola Babiak, Jozef Barunik
Citations: N/A
options implied volatilityq-fin
ARXIV · 2021 · arXiv
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers asset-liability liquidity risk management (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal to develop mathematical and statistical approaches, and provide appropriate answers. In this first part that focuses on liability liquidity risk modeling, we propose several statistical models for estimating redemption shocks. The historical approach must be complemented by an analytical approach based on zero-inflated models if we want to understand the true parameters that influence the redemption shocks. Moreover, we must also distinguish aggregate population models and individual-based models if we want to develop behavioral approaches. Once these different statistical models are calibrated, the second big issue is the risk measure to assess normal and stressed redemption shocks. Finally, the last issue is to develop a factor model that can translate stress scenarios on market risk factors into stress scenarios on fund liabilities.
Authors: Thierry Roncalli, Fatma Karray-Meziou, François Pan, Margaux Regnault
Citations: N/A
funding liquidityq-fin
ARXIV · 2020 · arXiv
Before the 2008 financial crisis, most research in financial mathematics focused on pricing options without considering the effects of counterparties' defaults, illiquidity problems, and the role of the sale and repurchase agreement (Repo) market. Recently, models were proposed to address this by computing a total valuation adjustment (XVA) of derivatives; however without considering a potential crisis in the market. In this article, we include a possible crisis by using an alternating renewal process to describe the switching between a normal financial regime and a financial crisis. We develop a framework to price the XVA of a European claim in this state-dependent situation. The price is characterized as a solution to a backward stochastic differential equation (BSDE), and we prove the existence and uniqueness of this solution. In a numerical study based on a deep learning algorithm for BSDEs, we compare the effect of different parameters on the valuation of the XVA.
Authors: Weijie Pang, Stephan Sturm
Citations: N/A
repo marketq-fin
OPENALEX · 2020 · Speech
Remarks at Brookings-Chicago Booth Task Force on Financial Stability (TFFS) meeting, panel on market liquidity (delivered via videoconference).
Authors: Lorie Logan
Citations: 31
Insurance and Financial Risk ManagementState Capitalism and Financial GovernanceBanking stability, regulation, efficiency
ARXIV · 2020 · arXiv
We test various volatility models using the Bitcoin spot price series. Our models include HIST, EMA ARCH, GARCH, and EGARCH, models. Both of our in-sample-fit and out-of-sample-forecast results suggest that GARCH and EGARCH models perform much better than other models. Moreover, the EGARCH model's asymmetric term is positive and insignificant, which suggests that Bitcoin prices lack the asymmetric volatility response to past returns. Finally, we formulate an option trading strategy by exploiting the volatility spread between the GARCH volatility forecast and the option's implied volatility. We show that a simple volatility-spread trading strategy with delta-hedging can yield robust profits.
Authors: Yeguang Chi, Wenyan Hao
Citations: N/A
options implied volatilityq-fin
OPENALEX · 2020 · European Journal of Finance
Over the last decade, the foreign exchange derivatives market has witnessed a collapse of covered interest parity (CIP). Not only does this collapse give rise to large deviations from CIP, it has unlocked a stream of exploitable arbitrage opportunities across currencies. In this paper, we introduce two new factors – inflation differential and relative economic performance – as potential drivers of deviations from CIP. Employing data on G10 cross-currency basis swap spreads viz a viz the U.S. dollar, we document a striking new evidence that higher inflation differential and incremental improvement in relative economic performance drive the basis wider, and hence arbitrage profits higher for U.S. dollar-based investors, in the post crisis period. Our main empirical results in general are robust to an extended number of controls, variations in sampling frequency, and consideration of alternative specifications, but the additional explanatory power is low.
Authors: Oyakhilome Ibhagui
Citations: 6
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyGlobal Financial Crisis and Policies
OPENALEX · 2020 · Proceedings of the AAAI Conference on Artificial Intelligence
In recent years, considerable efforts have been devoted to developing AI techniques for finance research and applications. For instance, AI techniques (e.g., machine learning) can help traders in quantitative trading (QT) by automating two tasks: market condition recognition and trading strategies execution. However, existing methods in QT face challenges such as representing noisy high-frequent financial data and finding the balance between exploration and exploitation of the trading agent with AI techniques. To address the challenges, we propose an adaptive trading model, namely iRDPG, to automatically develop QT strategies by an intelligent trading agent. Our model is enhanced by deep reinforcement learning (DRL) and imitation learning techniques. Specifically, considering the noisy financial data, we formulate the QT process as a Partially Observable Markov Decision Process (POMDP). Also, we introduce imitation learning to leverage classical trading strategies useful to balance between exploration and exploitation. For better simulation, we train our trading agent in the real financial market using minute-frequent data. Experimental results demonstrate that our model can extract robust market features and be adaptive in different markets.
Authors: Yang Liu, Qi Liu, Hongke Zhao, Pan Zhen, Chuanren Liu
Citations: 133
Stock Market Forecasting MethodsFinancial Markets and Investment StrategiesComplex Systems and Time Series Analysis
ARXIV · 2020 · arXiv
The VSTOXX index tracks the expected 30-day volatility of the EURO STOXX 50 equity index. Futures on the VSTOXX index can, therefore, be used to hedge against economic uncertainty. We investigate the effect of trader inventory on the price of VSTOXX futures through a combination of stochastic processes and machine learning methods. We formulate a simple and efficient pricing methodology for VSTOXX futures, which assumes a Heston-type stochastic process for the underlying EURO STOXX 50 market. Under these dynamics, approximate analytical formulas for the implied volatility smile and the VSTOXX index have recently been derived. We use the EURO STOXX 50 option implied volatilities and the VSTOXX index value to estimate the parameters of this Heston model. Following the calibration, we calculate theoretical VSTOXX future prices and compare them to the actual market prices. While theoretical and market prices are usually in line, we also observe time periods, during which the market price does not agree with our Heston model. We collect a variety of market features that could potentially explain the price deviations and calibrate two machine learning models to the price difference: a regularized linear model and a random forest. We find that both models indicate a strong influence of accumulated trader positions on the VSTOXX futures price.
Authors: Daniel Guterding
Citations: N/A
options implied volatilityq-fin
OPENALEX · 2020 · Applied Sciences
In quantitative trading, stock prediction plays an important role in developing an effective trading strategy to achieve a substantial return. Prediction outcomes also are the prerequisites for active portfolio construction and optimization. However, the stock prediction is a challenging task because of the diversified factors involved such as uncertainty and instability. Most of the previous research focuses on analyzing financial historical data based on statistical techniques, which is known as a type of time series analysis with limited achievements. Recently, deep learning techniques, specifically recurrent neural network (RNN), has been designed to work with sequence prediction. In this paper, a long short-term memory (LSTM) network, which is a special kind of RNN, is proposed to predict stock movement based on historical data. In order to construct an efficient portfolio, multiple portfolio optimization techniques, including equal-weighted modeling (EQ), simulation modeling Monte Carlo simulation (MCS), and optimization modeling mean variant optimization (MVO), are used to improve the portfolio performance. The results showed that our proposed LSTM prediction model works efficiently by obtaining high accuracy from stock prediction. The constructed portfolios based on the LSTM prediction model outperformed other constructed portfolios-based prediction models such as linear regression and support vector machine. In addition, optimization techniques showed a significant improvement in the return and Sharpe ratio of the constructed portfolios. Furthermore, our constructed portfolios beat the benchmark Standard and Poor 500 (S&P 500) index in both active returns and Sharpe ratios.
Authors: Van-Dai Ta, Chuan-Ming Liu, Direselign Addis Tadesse
Citations: 186
Stock Market Forecasting MethodsFinancial Markets and Investment StrategiesEnergy Load and Power Forecasting
SEMANTIC SCHOLAR · 2020 · Journal of international financial markets, institutions, and money
Abstract We introduce an affine term structure model with observed macroeconomic factors for credit spread curves under the unconventional monetary policy regime in Japan. Empirical results based on the model selection using Japanese data demonstrate that the credit spread curves are dominated by the monetary policy and suggest that global economic forces, such as the U.S. Treasury yield and Baa-Aaa credit spread, play a major role in the dynamics of credit spread curves, complementing a growing body of literature explaining what drives credit spread curves. Our contemporaneous response and historical decomposition analyses find that monetary policy and global economic and financial forces have large impacts on credit spread curves at all maturities and rating classes.
Authors: Tatsuyoshi Okimoto, Sumiko Takaoka
Citations: 6
creditcredit spread decomposition
ARXIV · 2019 · arXiv
Systemic liquidity risk, defined by the IMF as "the risk of simultaneous liquidity difficulties at multiple financial institutions", is a key topic in macroprudential policy and financial stress analysis. Specialized models to simulate funding liquidity risk and contagion are available but they require not only banks' bilateral exposures data but also balance sheet data with sufficient granularity, which are hardly available. Alternatively, risk analyses on interbank networks have been done via centrality measures of the underlying graph capturing the most interconnected and hence more prone to risk spreading banks. In this paper, we propose a model which relies on an epidemic model which simulate a contagion on the interbank market using the funding liquidity shortage mechanism as contagion process. The model is enriched with country and bank risk features which take into account the heterogeneity of the interbank market. The proposed model is particularly useful when full set of data necessary to run specialized models is not available. Since the interbank network is not fully available, an economic driven reconstruction method is also proposed to retrieve the interbank network by constraining the standard reconstruction methodology to real financial indicators. We show that the contagion model is able to reproduce systemic liquidity risk across different years and countries. This result suggests that the proposed model can be successfully used as a valid alternative to more complex ones.
Authors: V. Macchiati, G. Brandi, G. Cimini, G. Caldarelli, D. Paolotti, T. Di Matteo
Citations: N/A
funding liquidityq-fin
OPENALEX · 2019 · Review of Financial Studies
Abstract According to our survey about climate risk perceptions, institutional investors believe climate risks have financial implications for their portfolio firms and that these risks, particularly regulatory risks, already have begun to materialize. Many of the investors, especially the long-term, larger, and ESG-oriented ones, consider risk management and engagement, rather than divestment, to be the better approach for addressing climate risks. Although surveyed investors believe that some equity valuations do not fully reflect climate risks, their perceived overvaluations are not large.
Authors: Philipp Krueger, Zacharias Sautner, Laura T. Starks
Citations: 2820
Sustainable Finance and Green BondsFinancial Markets and Investment StrategiesMarket Dynamics and Volatility
OPENALEX · 2019 · Monash University Research Portal (Monash University)
Abstract: SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
Authors: Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson
Citations: 11591
Computational Physics and Python ApplicationsScientific Computing and Data ManagementParticle physics theoretical and experimental studies
ARXIV · 2019 · arXiv
Low-frequency historical data, high-frequency historical data and option data are three major sources, which can be used to forecast the underlying security's volatility. In this paper, we propose two econometric models, which integrate three information sources. In GARCH-Itô-OI model, we assume that the option-implied volatility can influence the security's future volatility, and the option-implied volatility is treated as an observable exogenous variable. In GARCH-Itô-IV model, we assume that the option-implied volatility can not influence the security's volatility directly, and the relationship between the option-implied volatility and the security's volatility is constructed to extract useful information of the underlying security. After providing the quasi-maximum likelihood estimators for the parameters and establishing their asymptotic properties, we also conduct a series of simulation analysis and empirical analysis to compare the proposed models with other popular models in the literature. We find that when the sampling interval of the high-frequency data is 5 minutes, the GARCH-Itô-OI model and GARCH-Itô-IV model has better forecasting performance than other models.
Authors: Huiling Yuan, Yong Zhou, Zhiyuan Zhang, Xiangyu Cui
Citations: N/A
options implied volatilityq-fin
ARXIV · 2019 · arXiv
We present a formulation of the transaction cost analysis (TCA) in the Bayesian framework for the primary purpose of comparing broker algorithms using standardized benchmarks. Our formulation allows effective calculation of the expected value of trading benchmarks with only a finite sample of data relevant to practical applications. We discuss the nature of distribution of implementation shortfall, volume-weighted average price, participation-weighted price and short-term reversion benchmarks. Our model takes into account fat tails, skewness of the distributions and heteroscedasticity of benchmarks. The proposed framework allows the use of hierarchical models to transfer approximate knowledge from a large aggregated sample of observations to a smaller sample of a particular algorithm.
Authors: Vladimir Markov
Citations: N/A
transaction cost analysisq-fin
ARXIV · 2019 · arXiv
The electronic platform has been increasingly popular for executing large corporate bond orders by asset managers, who in turn have to assess the quality of their executions via Transaction Cost Analysis (TCA). One of the challenges in TCA is to build a realistic benchmark for the expected transaction cost and to characterize the price impact of each individual trade with given bond characteristics and market conditions. Taking the viewpoint of retail investors, this paper presents an analytical methodology for TCA of corporate bond trading. Our analysis is based on the TRACE Enhanced dataset; and starts with estimating the initiator of a bond transaction, followed by estimating the bid-ask spread and the mid-price dynamics. With these estimations, the first part of our study is to identify key features for corporate bonds and to compute the expected average trading cost. This part is on the time scale of weekly transactions, and is by applying and comparing several regularized regression models. The second part of our study is using the estimated mid-price dynamics to investigate the amplitude of its price impact and the decay pattern of individual bond transaction. This part is on the time scale of each transaction of liquid corporate bonds, and is by applying a transient impact model to estimate the price impact kernel using a non-parametric method. Our benchmark model allows for identifying abnormal transactions and for enhancing counter-party selections. A key discovery of our study is the price impact asymmetry between customer-buy orders and consumer-sell orders.
Authors: Xin Guo, Charles-Albert Lehalle, Renyuan Xu
Citations: N/A
transaction cost analysisq-fin
ARXIV · 2017 · arXiv
We discuss the binary nature of funding impact in derivative valuation. Under some conditions, funding is either a cost or a benefit, i.e., one of the lending/borrowing rates does not play a role in pricing derivatives. When derivatives are priced, considering different lending/borrowing rates leads to semi-linear BSDEs and PDEs, and thus it is necessary to solve the equations numerically. However, once it can be guaranteed that only one of the rates affects pricing, linear equations can be recovered and analytical formulae can be derived. Moreover, as a byproduct, our results explain how debt value adjustment (DVA) and funding benefits are dissimilar. It is often believed that considering both DVA and funding benefits results in a double-counting issue but it will be shown that the two components are affected by different mathematical structures of derivative transactions. We find that funding benefit is related to the decreasing property of the payoff function, but this relationship decreases as the funding choices of underlying assets are transferred to repo markets.
Authors: Junbeom Lee, Chao Zhou
Citations: N/A
repo marketq-fin
ARXIV · 2017 · arXiv
Cash collateral is perfect in that it provides simultaneous counterparty credit risk protection and derivatives funding. Securities are imperfect collateral, because of collateral segregation or differences in CSA haircuts and repo haircuts. Moreover, the collateral rate term structure is not observable in the repo market, for derivatives netting sets are perpetual while repo tenors are typically in months. This article synthesizes these effects into a derivative financing rate that replaces the risk-free discount rate. A break-even repo formulae is employed to supply non-observable collateral rates, enabling collateral liquidity value adjustment (LVA) to be computed. A linear programming problem of maximizing LVA under liquidity coverage ratio (LCR) constraint is formulated as a core algorithm of collateral optimization. Numerical examples show that LVA could be sizable for long average duration, deep in or out of the money swap portfolios.
Authors: Wujiang Lou
Citations: N/A
repo marketq-fin
OPENALEX · 2017 · Brookings Papers on Economic Activity
We propose three core principles that should inform the design of bank capital regulation. First, whenever possible, multiple constraints on the minimum level of equity capital should be consolidated into a single constraint. This helps to avoid a distortionary situation where different constraints bind for different banks performing the same activity. Second, the best way to deal with the inevitable gaming of any set of ex ante capital rules is not to propose further rules, but rather to allow the regulator sufficient flexibility to address unforeseen contingencies ex post. Third, though a regulatory framework that relies primarily on minimum capital ratios is appropriate for normal times, such a framework is inadequate in the wake of a large negative shock to the system. Following an adverse shock, it becomes critical to emphasize dynamic resilience, which involves forcing banks to actively recapitalize-that is, regulation needs to focus on getting banks to raise new dollars of equity capital, rather than just maintaining their capital ratios. Applying these principles, we suggest a number of modifications to the current set of risk-based capital requirements, to the leverage ratio, and to the Federal Reserve's stress-testing framework.
Authors: Robin Greenwood, Jeremy C. Stein, Samuel Hanson, Adi Sunderam
Citations: 107
Banking stability, regulation, efficiencyCredit Risk and Financial RegulationsGlobal Financial Crisis and Policies
ARXIV · 2016 · arXiv
Testing procedures for predictive regressions with lagged autoregressive variables imply a suboptimal inference in presence of small violations of ideal assumptions. We propose a novel testing framework resistant to such violations, which is consistent with nearly integrated regressors and applicable to multi-predictor settings, when the data may only approximately follow a predictive regression model. The Monte Carlo evidence demonstrates large improvements of our approach, while the empirical analysis produces a strong robust evidence of market return predictability hidden by anomalous observations, both in- and out-of-sample, using predictive variables such as the dividend yield or the volatility risk premium.
Authors: Lorenzo Camponovo, Olivier Scaillet, Fabio Trojani
Citations: N/A
volatility risk premiumq-fin
OPENALEX · 2016 · Review of International Political Economy
In its capacity as debt issuer, the state has played a growing role in financial life over the last 30 years. To examine this role and connect it to shadow banking, the paper develops the concept of the ‘repo trinity’, which captures a set of policy objectives that central banks outlined after the 1998 Russian crisis, the first systemic crisis of collateral-based finance. The repo trinity connected financial stability with liquid government bond markets and free repo markets. It further reinforced the dominance of the US government bond market as institutional template for states adjusting to a world of independent central banks, market-based financing and global competition for liquidity. Central banks and the Financial Stability Board recognized the impossible nature of the trinity after 2008, attributing cyclical leverage (financial instability) and elusive liquidity in collateral markets to deregulated repo markets, markets systemic to shadow banking. The new approach triggered radical changes in crisis central banking but has not powered significant regulatory interventions in the absence of an alternative mode of organizing government bond markets.
Authors: Daniela Gabor
Citations: 273
Banking stability, regulation, efficiencyHousing, Finance, and NeoliberalismEconomic Theory and Policy
OPENALEX · 2016 · Journal of Economic Surveys
Abstract This survey reviews the growing literature on pairs trading frameworks, i.e., relative‐value arbitrage strategies involving two or more securities. Research is categorized into five groups: The distance approach uses nonparametric distance metrics to identify pairs trading opportunities. The cointegration approach relies on formal cointegration testing to unveil stationary spread time series. The time‐series approach focuses on finding optimal trading rules for mean‐reverting spreads. The stochastic control approach aims at identifying optimal portfolio holdings in the legs of a pairs trade relative to other available securities. The category “other approaches” contains further relevant pairs trading frameworks with only a limited set of supporting literature. Finally, pairs trading profitability is reviewed in the light of market frictions. Drawing from a large set of research consisting of over 100 references, an in‐depth assessment of each approach is performed, ultimately revealing strengths and weaknesses relevant for further research and for implementation.
Authors: Christopher Krauß
Citations: 214
Financial Markets and Investment StrategiesCorporate Finance and GovernanceEconomic theories and models
OPENALEX · 2016 · Quantitative Finance
We perform an extensive and robust study of the performance of three different pairs trading strategies—the distance, cointegration and copula methods—on the entire US equity market from 1962 to 2014 with time-varying trading costs. For the cointegration and copula methods, we design a computationally efficient two-step pairs trading strategy. In terms of economic outcomes, the distance, cointegration and copula methods show a mean monthly excess return of 91, 85 and 43 bps (38, 33 and 5 bps) before transaction costs (after transaction costs), respectively. In terms of continued profitability, from 2009, the frequency of trading opportunities via the distance and cointegration methods is reduced considerably, whereas this frequency remains stable for the copula method. Further, the copula method shows better performance for its unconverged trades compared to those of the other methods. While the liquidity factor is negatively correlated to all strategies’ returns, we find no evidence of their correlation to market excess returns. All strategies show positive and significant alphas after accounting for various risk-factors. We also find that in addition to all strategies performing better during periods of significant volatility, the cointegration method is the superior strategy during turbulent market conditions.
Authors: Hossein Rad, Rand Kwong Yew Low, Robert W. Faff
Citations: 154
Financial Markets and Investment StrategiesMarket Dynamics and VolatilityFinancial Risk and Volatility Modeling
OPENALEX · 2016 · Journal of Financial and Quantitative Analysis
Abstract We provide new empirical evidence that U.S. expected growth and consumption volatility are closely related to the strong comovement in sovereign spreads. We rationalize these findings in an equilibrium model with recursive utility for credit default swap (CDS) spreads. The framework links a reduced-form default process with country-specific sensitivity to expected growth and macroeconomic uncertainty. Exploiting the high-frequency information in the CDS term structure across 38 countries, we estimate the model and find parameters consistent with preference for early resolution of uncertainty. Our results confirm the existence of time-varying risk premia in sovereign spreads as compensation for exposure to common U.S. macroeconomic risk.
Authors: Patrick Augustin, Roméo Tédongap
Citations: 123
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyGlobal Financial Crisis and Policies
ARXIV · 2016 · arXiv
We take the holistic approach of computing an OTC claim value that incorporates credit and funding liquidity risks and their interplays, instead of forcing individual price adjustments: CVA, DVA, FVA, KVA. The resulting nonlinear mathematical problem features semilinear PDEs and FBSDEs. We show that for the benchmark vulnerable claim there is an analytical solution, and we express it in terms of the Black-Scholes formula with dividends. This allows for a detailed valuation analysis, stress testing and risk analysis via sensitivities.
Authors: Damiano Brigo, Cristin Buescu, Marek Rutkowski
Citations: N/A
funding liquidityq-fin
OPENALEX · 2016 · BIS quarterly review
Covered interest parity verges on a physical law in international finance. And yet it has been systematically violated since the Great Financial Crisis. Especially puzzling have been the violations since 2014, even once banks had strengthened their balance sheets and regained easy access to funding. We offer a framework to think about these violations, stressing the combination of hedging demand and tighter limits to arbitrage, which in turn reflect a tighter management of risks and bank balance sheet constraints. We find empirical support for this framework both across currencies and over time.
Authors: Claudio Borio, Robert N. McCauley, Patrick McGuire, Vladyslav Sushko
Citations: 151
Banking stability, regulation, efficiencyGlobal Financial Crisis and PoliciesCredit Risk and Financial Regulations
OPENALEX · 2016 · RePEc: Research Papers in Economics
The cross-currency basis, which is the basis spread added mainly to the U.S. dollar London Interbank Offered Rate (USD LIBOR) when the USD is funded via foreign exchange (FX) swaps using the Japanese yen or the euro as a funding currency, has been widening globally since the beginning of 2014. This development is driven by (1) increased demands for U.S. dollars resulting from a divergence in the monetary policy between the U.S. and other advanced countries, (2) global banks' reduced appetite for market-making and arbitrage due to regulatory reforms, and (3) the decrease in the supply of U.S. dollars from foreign reserve managers/sovereign wealth funds against the background of declines in commodity prices and emerging currency depreciations.
Authors: Fumihiko Arai, Yoshibumi Makabe, Yasunori Okawara, Teppei Nagano
Citations: 19
Global Financial Crisis and Policies
OPENALEX · 2015 · Review of Financial Studies
Hundreds of papers and factors attempt to explain the cross-section of expected returns. Given this extensive data mining, it does not make sense to use the usual criteria for establishing significance. Which hurdle should be used for current research? Our paper introduces a new multiple testing framework and provides historical cutoffs from the first empirical tests in 1967 to today. A new factor needs to clear a much higher hurdle, with a t-statistic greater than 3.0. We argue that most claimed research findings in financial economics are likely false. (JEL C12, C52, G12)
Authors: Campbell R. Harvey, Yan Liu, Caroline Zhu
Citations: 1978
Financial Markets and Investment StrategiesStochastic processes and financial applicationsFinancial Risk and Volatility Modeling
OPENALEX · 2015 · Review of Financial Studies
The search for a market design that ensures stable bank funding is at the top of regulators' policy agenda. This paper empirically shows that the central counterparty (CCP)-based euro interbank repo market features this stability. Using a unique and comprehensive data set, we show that the market is resilient during crisis episodes and may even act as a shock absorber, in the sense that repo lending increases with risk, while spreads, maturities, and haircuts remain stable. Our comparison across different repo markets shows that anonymous CCP-based trading, safe collateral, and the absence of an unwind mechanism are the key characteristics to ensure market resilience. Received October 22, 2014; accepted July 28, 2015 by Editor Stefan Nagel.
Authors: Loriano Mancini, Angelo Ranaldo, Jan Wrampelmeyer
Citations: 137
Banking stability, regulation, efficiencyGlobal Financial Crisis and PoliciesCredit Risk and Financial Regulations
ARXIV · 2015 · arXiv
Collateralization with daily margining has become a new standard in the post-crisis market. Although there appeared vast literature on a so-called multi-curve framework, a complete picture of a multi-currency setup with cross-currency basis can be rarely found since our initial attempts. This work gives its extension regarding a general framework of interest rates in a fully collateralized market. It gives a new formulation of the currency funding spread which is better suited for the general dependence. In the last half, it develops a discretization of the HJM framework with a fixed tenor structure, which makes it implementable as a traditional Market Model.
Authors: Masaaki Fujii, Akihiko Takahashi
Citations: N/A
cross currency basisq-fin
OPENALEX · 2015 · The Quarterly Journal of Economics
Abstract The high-frequency trading arms race is a symptom of flawed market design. Instead of the continuous limit order book market design that is currently predominant, we argue that financial exchanges should use frequent batch auctions: uniform price double auctions conducted, for example, every tenth of a second. That is, time should be treated as discrete instead of continuous, and orders should be processed in a batch auction instead of serially. Our argument has three parts. First, we use millisecond-level direct-feed data from exchanges to document a series of stylized facts about how the continuous market works at high-frequency time horizons: (i) correlations completely break down; which (ii) leads to obvious mechanical arbitrage opportunities; and (iii) competition has not affected the size or frequency of the arbitrage opportunities, it has only raised the bar for how fast one has to be to capture them. Second, we introduce a simple theory model which is motivated by and helps explain the empirical facts. The key insight is that obvious mechanical arbitrage opportunities, like those observed in the data, are built into the market design—continuous-time serial-processing implies that even symmetrically observed public information creates arbitrage rents. These rents harm liquidity provision and induce a never-ending socially wasteful arms race for speed. Last, we show that frequent batch auctions directly address the flaws of the continuous limit order book. Discrete time reduces the value of tiny speed advantages, and the auction transforms competition on speed into competition on price. Consequently, frequent batch auctions eliminate the mechanical arbitrage rents, enhance liquidity for investors, and stop the high-frequency trading arms race.
Authors: Eric Budish, Peter Cramton, John J. Shim
Citations: 893
Financial Markets and Investment StrategiesAuction Theory and ApplicationsComplex Systems and Time Series Analysis
ARXIV · 2015 · arXiv
We derive representations of local risk-minimization of call and put options for Barndorff-Nielsen and Shephard models: jump type stochastic volatility models whose squared volatility process is given by a non-Gaussian rnstein-Uhlenbeck process. The general form of Barndorff-Nielsen and Shephard models includes two parameters: volatility risk premium $β$ and leverage effect $ρ$. Arai and Suzuki (2015, arxiv:1503.08589) dealt with the same problem under constraint $β=-\frac{1}{2}$. In this paper, we relax the restriction on $β$; and restrict $ρ$ to $0$ instead. We introduce a Malliavin calculus under the minimal martingale measure to solve the problem.
Authors: Takuji Arai
Citations: N/A
volatility risk premiumq-fin
ARXIV · 2015 · arXiv
A new modelling approach that directly prescribes dynamics to the term structure of VIX futures is proposed in this paper. The approach is motivated by the tractability enjoyed by models that directly prescribe dynamics to the VIX, practices observed in interest-rate modelling, and the desire to develop a platform to better understand VIX option implied volatilities. The main contribution of the paper is the derivation of necessary conditions for there to be no arbitrage between the joint market of VIX and equity derivatives. The arbitrage conditions are analogous to the well-known HJM drift restrictions in interest-rate modelling. The restrictions also address a fundamental open problem related to an existing modelling approach, in which the dynamics of the VIX are specified directly. The paper is concluded with an application of the main result, which demonstrates that when modelling VIX futures directly, the drift and diffusion of the corresponding stochastic volatility model must be restricted to preclude arbitrage.
Authors: Alexander Badran, Beniamin Goldys
Citations: N/A
options implied volatilityq-fin
OPENALEX · 2014 · Quantitative Finance
In this book, Andrew Ang seeks to provide common rules and guidance for investment decisions to ‘nations, through sovereign wealth funds, collective owners like pension funds, endowments, and found...
Authors: Robert M. Anderson
Citations: 91
State Capitalism and Financial Governance
OPENALEX · 2014 · European Finance Review
Abstract Following the “flash crash” on May 6, 2010, warning signals for impending market stress have been in high demand, yet only the VPIN metric of Easley, López de Prado, and O’Hara (ELO) has claimed success. In addition, ELO find the metric useful in predicting short-term volatility. VPIN involves decomposing volume into active buys and sells. We utilize quotes and trade data to construct an accurate trade classification measure for E-mini S&P 500 futures. Against this benchmark, the ELO Bulk Volume Classification (BVC) scheme is inferior to a standard tick rule. Moreover, VPIN predicts volatility solely because increasing volatility induces systematic classification errors in the BVC procedure. We conclude that VPIN is unsuitable for capturing order flow toxicity or signaling ensuing market turbulence.
Authors: Torben G. Andersen, Oleg Bondarenko
Citations: 64
Financial Risk and Volatility ModelingComplex Systems and Time Series AnalysisMarket Dynamics and Volatility
OPENALEX · 2014 · The Journal of Finance
ABSTRACT The repo market has been viewed as a potential source of financial instability since the 2007 to 2009 financial crisis, based in part on findings that margins increased sharply in a segment of this market. This paper provides evidence suggesting that there was no system‐wide run on repo. Using confidential data on tri‐party repo, a major segment of this market, we show that, the level of margins and the amount of funding were surprisingly stable for most borrowers during the crisis. However, we also document a sharp decline in the tri‐party repo funding of Lehman in September 2008.
Authors: Adam Copeland, Antoine Martin, Michael Walker
Citations: 325
Banking stability, regulation, efficiencyInsurance and Financial Risk ManagementHousing Market and Economics
OPENALEX · 2014 · The Journal of Finance
ABSTRACT We model a loop between sovereign and bank credit risk. A distressed financial sector induces government bailouts, whose cost increases sovereign credit risk. Increased sovereign credit risk in turn weakens the financial sector by eroding the value of its government guarantees and bond holdings. Using credit default swap (CDS) rates on European sovereigns and banks, we show that bailouts triggered the rise of sovereign credit risk in 2008. We document that post‐bailout changes in sovereign CDS explain changes in bank CDS even after controlling for aggregate and bank‐level determinants of credit spreads, confirming the sovereign‐bank loop.
Authors: Viral V. Acharya, Itamar Drechsler, Philipp Schnabl
Citations: 823
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyGlobal Financial Crisis and Policies
OPENALEX · 2014 · Review of Financial Studies
We examine the role of high-frequency traders (HFTs) in price discovery and price efficiency. Overall HFTs facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors, both on average and on the highest volatility days. This is done through their liquidity demanding orders. In contrast, HFTs' liquidity supplying orders are adversely selected. The direction of HFTs' trading predicts price changes over short horizons measured in seconds. The direction of HFTs' trading is correlated with public information, such as macro news announcements, market-wide price movements, and limit order book imbalances.
Authors: Jonathan Brogaard, Terrence Hendershott, Ryan Riordan
Citations: 1218
Financial Markets and Investment StrategiesFinancial Risk and Volatility ModelingComplex Systems and Time Series Analysis
ARXIV · 2014 · arXiv
For a commodity spot price dynamics given by an Ornstein-Uhlenbeck process with Barndorff-Nielsen and Shephard stochastic volatility, we price forwards using a class of pricing measures that simultaneously allow for change of level and speed in the mean reversion of both the price and the volatility. The risk premium is derived in the case of arithmetic and geometric spot price processes, and it is demonstrated that we can provide flexible shapes that is typically observed in energy markets. In particular, our pricing measure preserves the affine model structure and decomposes into a price and volatility risk premium, and in the geometric spot price model we need to resort to a detailed analysis of a system of Riccati equations, for which we show existence and uniqueness of solution and asymptotic properties that explains the possible risk premium profiles. Among the typical shapes, the risk premium allows for a stochastic change of sign, and can attain positive values in the short end of the forward market and negative in the long end.
Authors: Fred Espen Benth, Salvador Ortiz-Latorre
Citations: N/A
volatility risk premiumq-fin
ARXIV · 2014 · arXiv
We revisit the problem of pricing options with historical volatility estimators. We do this in the context of a generalized GARCH model with multiple time scales and asymmetry. It is argued that the reason for the observed volatility risk premium is tail risk aversion. We parametrize such risk aversion in terms of three coefficients: convexity, skew and kurtosis risk premium. We propose that option prices under the real-world measure are not martingales, but that their drift is governed by such tail risk premia. We then derive a fair-pricing equation for options and show that the solutions can be written in terms of a stochastic volatility model in continuous time and under a martingale probability measure. This gives a precise connection between the pricing and real-world probability measures, which cannot be obtained using Girsanov Theorem. We find that the convexity risk premium, not only shifts the overall implied volatility level, but also changes its term structure. Moreover, the skew risk premium makes the skewness of the volatility smile steeper than a pure historical estimate. We derive analytical formulas for certain implied moments using the Bergomi-Guyon expansion. This allows for very fast calibrations of the models. We show examples of a particular model which can reproduce the observed SPX volatility surface using very few parameters.
Authors: Samuel E. Vazquez
Citations: N/A
volatility risk premiumq-fin
ARXIV · 2013 · arXiv
Through the analysis of a dataset of ultra high frequency order book updates, we introduce a model which accommodates the empirical properties of the full order book together with the stylized facts of lower frequency financial data. To do so, we split the time interval of interest into periods in which a well chosen reference price, typically the mid price, remains constant. Within these periods, we view the limit order book as a Markov queuing system. Indeed, we assume that the intensities of the order flows only depend on the current state of the order book. We establish the limiting behavior of this model and estimate its parameters from market data. Then, in order to design a relevant model for the whole period of interest, we use a stochastic mechanism that allows for switches from one period of constant reference price to another. Beyond enabling to reproduce accurately the behavior of market data, we show that our framework can be very useful for practitioners, notably as a market simulator or as a tool for the transaction cost analysis of complex trading algorithms.
Authors: Weibing Huang, Charles-Albert Lehalle, Mathieu Rosenbaum
Citations: N/A
transaction cost analysisq-fin
OPENALEX · 2013 · Journal of Financial and Quantitative Analysis
Abstract Our objective in this paper is to examine whether one can use option-implied information to improve the selection of mean-variance portfolios with a large number of stocks, and to document which aspects of option-implied information are most useful to improve their out-of-sample performance. Portfolio performance is measured in terms of volatility, Sharpe ratio, and turnover. Our empirical evidence shows that using option-implied volatility helps to reduce portfolio volatility. Using option-implied correlation does not improve any of the metrics. Using option-implied volatility, risk premium, and skewness to adjust expected returns leads to a substantial improvement in the Sharpe ratio, even after prohibiting short sales and accounting for transaction costs.
Authors: Victor DeMiguel, Yuliya Plyakha, Raman Uppal, Grigory Vilkov
Citations: 243
Financial Markets and Investment StrategiesStochastic processes and financial applicationsCorporate Finance and Governance
ARXIV · 2013 · arXiv
We make several improvements to the mean-variance framework for optimal pre-trade algorithmic execution, by working with volume measures and generic price dynamics. Volume measures are the continuum analogies for discrete volume profiles commonly implemented in the execution industry. Execution then becomes an absolutely continuous measure over such a measure space, and its Radon-Nikodym derivative is commonly known as the Participation of Volume (PoV) function. The four impact cost components are all consistently built upon the PoV function. Some novel efforts are made for these linear impact models by having market signals more properly expressed. For the opportunistic cost, we are able to go beyond the conventional Brownian-type motions. By working directly with the auto-covariances of the price dynamics, we remove the Markovian restriction associated with Brownians and thus allow potential memory effects in the price dynamics. In combination, the final execution model becomes a constrained quadratic programming problem in infinite-dimensional Hilbert spaces. Important linear constraints such as participation capping are all permissible. Uniqueness and existence of optimal solutions are established via the theory of positive compact operators in Hilbert spaces. Several typical numerical examples explain both the behavior and versatility of the model.
Authors: Jackie Jianhong Shen
Citations: N/A
algorithmic executionq-fin
ARXIV · 2013 · arXiv
Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance, a stochastic control, an impulse control or a statistical learning viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, called anomaly detectors, can be created for each market order. We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts which of the orders are underperforming while calculating the predictive power of this explanatory factor set. Armed with this information, which we call influence analysis, we intend to empower the order monitoring user to take appropriate action on any affected orders by re-calibrating the trading algorithms working the order through new parameters, pausing their execution or taking over more direct trading control. Also we intend that use of this method in the post trade analysis of algorithms can be taken advantage of to automatically adjust their trading action.
Authors: Robert Azencott, Arjun Beri, Yutheeka Gadhyan, Nicolas Joseph, Charles-Albert Lehalle, Matthew Rowley
Citations: N/A
transaction cost analysisq-fin
OPENALEX · 2012 · Federal Reserve Bank of New York Economic policy review
1. INTRODUCTION During the financial crisis of 2007-09, particularly around the time of the Bear Stearns and Lehman Brothers failures, it became apparent that weaknesses existed in the design of the U.S. tri-party repo market, used by major broker-dealers to finance their inventories of securities. These design weaknesses had the potential to rapidly elevate and propagate systemic risk. Following the crisis, an industry-led effort sponsored by the Federal Reserve Bank of New York was undertaken to improve the tri-party repo market's infrastructure, with the main goal of lowering systemic risk. This article describes some key mechanics of the market--in particular, the collateral allocation process and the process--that have contributed to the market's fragility and delayed the reforms. A repurchase agreement, or is effectively a collateralized loan. A well-functioning tri-party repo market depends on the ability to efficiently allocate a dealer's securities--the collateral in the transaction--to the various repos that finance those securities. In the United States, collateral allocation currently involves considerable intervention by dealers, which slows the entire process. Collateral allocation is also complicated by the need for coordination between the Fixed Income Clearing Corporation (FICC), which clears some interdealer repos, and the clearing bank, which facilitates the settlement of tri-party repos. The length of time necessary to allocate collateral in the tri-party repo market has been a significant obstacle to market reform. Another impediment to reform is the unwind process, the settlement of expiring repos that occurs before new repos can be settled. The unwind creates a need for intraday funding to tide dealers over in the period between when they return cash to investors and when they get new cash from the settlement of new repos. In the tri-party repo market, this intraday financing is provided by the clearing banks. The dealers' reliance on intraday credit is one of the three weaknesses of the market highlighted in a Federal Reserve Bank of New York white paper on infrastructure reform. Such reliance creates potentially perverse dynamics that increase market fragility and financial system risk. The next section offers a brief overview of the U.S. repo market and some of its important segments. In Section 3, we describe the market in more detail and summarize the concerns surrounding it. Section 4 reviews the mechanics of tri-party repo transactions; Section 5 concludes. 2. THE U.S. REPO MARKET A repo is the sale of a security, or a portfolio of securities, combined with an agreement to repurchase the security or portfolio on a specified future date at a prearranged price. Aside from some legal distinctions concerning bankruptcy treatment, (1) a repo is similar to a collateralized loan. Exhibit 1 shows a basic repo transaction. For the opening leg of the repo, an institution with cash to invest, the cash provider, purchases securities from an institution looking to borrow cash, the collateral provider. The market value of the securities purchased typically exceeds the value of the cash. The difference is called the haircut. For example, if a cash loan of $95 is backed by collateral that has a market value of $100, then the haircut is 5 percent. For the closing leg of the repo, which occurs at the term of the repo, the collateral provider repurchases the securities for $95 plus an amount corresponding to the interest rate on the transaction. In most segments of the U.S. repo market, at least one of the counterparties is a securities dealer. (2) Dealers use the repo market to finance their inventories of securities, among other purposes. In some cases, the collateral provider is a client of the dealer that wants to borrow cash. On these repos, the dealer is the cash provider. Repos involve a variety of other cash providers, including money market funds (MMFs), asset managers, securities lending agents, and investors looking to obtain specific securities as collateral in order to hedge or speculate based on changes in the market values of those securities. …
ARXIV · 2012 · arXiv
We propose a unified structural credit risk model incorporating both insolvency and illiquidity risks, in order to investigate how a firm's default probability depends on the liquidity risk associated with its financing structure. We assume the firm finances its risky assets by mainly issuing short- and long-term debt. Short-term debt can have either a discrete or a more realistic staggered tenor structure. At rollover dates of short-term debt, creditors face a dynamic coordination problem. We show that a unique threshold strategy (i.e., a debt run barrier) exists for short-term creditors to decide when to withdraw their funding, and this strategy is closely related to the solution of a non-standard optimal stopping time problem with control constraints. We decompose the total credit risk into an insolvency component and an illiquidity component based on such an endogenous debt run barrier together with an exogenous insolvency barrier.
Authors: Gechun Liang, Eva Lütkebohmert, Wei Wei
Citations: N/A
funding liquidityq-fin
OPENALEX · 2012 · Journal of Agricultural and Applied Economics
The first decade of the 21 st century has perhaps witnessed more structural change in commodity futures markets than all previous decades combined. Not only have trading volumes and open interest increased markedly, but this time period also saw historic changes in both trading and participants. The available literature indicates that the irrational and harmful impacts of the structural changes in commodity futures markets over the last decade have been minimal. In particular, there is little evidence that passive index investment caused a massive bubble in commodity futures prices. There is intriguing evidence of several other rational and beneficial impacts of the structural changes over the last decade. In particular, the expanding market participation may have decreased risk premiums, and hence, the cost of hedging, reduced price volatility, and better integrated commodity markets with financial markets.
Authors: Scott H. Irwin, Dwight R. Sanders
Citations: 218
Market Dynamics and VolatilityMonetary Policy and Economic ImpactNatural Resources and Economic Development
ARXIV · 2012 · arXiv
Trading large volumes of a financial asset in order driven markets requires the use of algorithmic execution dividing the volume in many transactions in order to minimize costs due to market impact. A proper design of an optimal execution strategy strongly depends on a careful modeling of market impact, i.e. how the price reacts to trades. In this paper we consider a recently introduced market impact model (Bouchaud et al., 2004), which has the property of describing both the volume and the temporal dependence of price change due to trading. We show how this model can be used to describe price impact also in aggregated trade time or in real time. We then solve analytically and calibrate with real data the optimal execution problem both for risk neutral and for risk averse investors and we derive an efficient frontier of optimal execution. When we include spread costs the problem must be solved numerically and we show that the introduction of such costs regularizes the solution.
Authors: Enzo Busseti, Fabrizio Lillo
Citations: N/A
algorithmic executionq-fin
OPENALEX · 2012 · American Economic Review
Using micro-level data, we construct a credit spread index with considerable predictive power for future economic activity. We decompose the credit spread into a component that captures firm-specific information on expected defaults and a residual component–– the excess bond premium. Shocks to the excess bond premium that are orthogonal to the current state of the economy lead to declines in economic activity and asset prices. An increase in the excess bond premium appears to reflect a reduction in the risk-bearing capacity of the financial sector, which induces a contraction in the supply of credit and a deterioration in macroeconomic conditions.
Authors: Simon Gilchrist, Egon Zakrajšek
Citations: 2242
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyMonetary Policy and Economic Impact
OPENALEX · 2012 · The Journal of Financial Research
Abstract We examine the impact of trading costs on pairs trading profitability in the U.S. equity market, 1963 to 2009. After controlling for commissions, market impact, and short selling fees, pairs trading remains profitable, albeit at much more modest levels. Specifically, we document a risk‐adjusted return of about 30 basis points per month among portfolios of well‐matched pairs that are formed within refined industry groups. Pairs trading exhibits a lower risk and lower return profile than a short‐term reversal strategy that sorts stocks relative to their industry peers. Notably, both these types of contrarian investing are largely unprofitable after 2002.
Authors: Binh Do, Robert W. Faff
Citations: 135
Financial Markets and Investment StrategiesCorporate Finance and GovernanceAuditing, Earnings Management, Governance
OPENALEX · 2012 · Review of Financial Studies
Order flow is toxic when it adversely selects market makers, who may be unaware they are providing liquidity at a loss. We present a new procedure to estimate flow toxicity based on volume imbalance and trade intensity (the VPIN toxicity metric). VPIN is updated in volume time, making it applicable to the high-frequency world, and it does not require the intermediate estimation of non-observable parameters or the application of numerical methods. It does require trades classified as buys or sells, and we develop a new bulk volume classification procedure that we argue is more useful in high-frequency markets than standard classification procedures. We show that the VPIN metric is a useful indicator of short-term, toxicity-induced volatility.
Authors: David Easley, Marcos López de Prado, Maureen O’Hara
Citations: 548
Financial Markets and Investment StrategiesMarket Dynamics and VolatilityCredit Risk and Financial Regulations
ARXIV · 2011 · arXiv
We develop a theory for the market impact of large trading orders, which we call metaorders because they are typically split into small pieces and executed incrementally. Market impact is empirically observed to be a concave function of metaorder size, i.e., the impact per share of large metaorders is smaller than that of small metaorders. We formulate a stylized model of an algorithmic execution service and derive a fair pricing condition, which says that the average transaction price of the metaorder is equal to the price after trading is completed. We show that at equilibrium the distribution of trading volume adjusts to reflect information, and dictates the shape of the impact function. The resulting theory makes empirically testable predictions for the functional form of both the temporary and permanent components of market impact. Based on the commonly observed asymptotic distribution for the volume of large trades, it says that market impact should increase asymptotically roughly as the square root of metaorder size, with average permanent impact relaxing to about two thirds of peak impact.
Authors: J. Doyne Farmer, Austin Gerig, Fabrizio Lillo, Henri Waelbroeck
Citations: N/A
algorithmic executionq-fin
OPENALEX · 2011 · Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)
The financial crisis and the ensuing recession have caused a sharp deterioration in public finances across advanced economies, raising investor concerns about sovereign risk. The concerns have so far mainly affected the euro area, where some countries have seen their credit ratings downgraded during 2009−11 and their funding costs rise sharply. Other countries have also been affected, but to a much lesser extent. Greater sovereign risk is already having adverse effects on banks and financial markets.\nLooking forward, sovereign risk concerns may affect a broad range of countries. In advanced economies, government debt levels are expected to rise over coming years, due to high fiscal deficits and rising pension and health care costs. In emerging economies, vulnerability to external shocks and political instability may have periodic adverse effects on sovereign risk. Overall, risk premia on government debt will likely be higher and more volatile than in the past. In some countries, sovereign debt has already lost its risk-free status; in others, it may do so in the future.\nThe challenge for authorities is to minimise the negative consequences for bank funding and the flow-on effects on the real economy. This report outlines the impact of sovereign risk concerns on the cost and availability of bank funding over recent years. It then describes the channels through which sovereign risk affects bank funding. The last section summarises the main conclusions and discusses some implications for banks and the official sector.\nTwo caveats are necessary before discussing the main findings. First, the analysis focuses on causality going from sovereigns to banks, as is already the case in some countries, and, looking forward, is a possible scenario for other economies. But causality may clearly also go from banks to sovereigns. However, even in this second case, sovereign risk eventually acquires its own dynamics and compounds the problems of the banking sector. Second, the report examines the link between sovereign risk and bank funding in general terms, based on recent experience and research. It does not assess actual sovereign risk and its impact on bank stability in individual countries at the present juncture.
Authors: Fabio Panetta, Ricardo Correa, Michael Davies, Antonio Di Cesare, José-Manuel Marques, Francisco Nadal De Simone, Federico Maria Signoretti, Cristina Vespro
Citations: 234
State Capitalism and Financial GovernanceGlobal Financial Crisis and Policies
OPENALEX · 2010 · Journal of Financial and Quantitative Analysis
Abstract In this paper, we identify jumps in U.S. Treasury-bond (T-bond) prices and investigate what causes such unexpected large price changes. In particular, we examine the relative importance of macroeconomic news announcements versus variation in market liquidity in explaining the observed jumps in the U.S. Treasury market. We show that while jumps occur mostly at prescheduled macroeconomic announcement times, announcement surprises have limited power in explaining bond price jumps. Our analysis further shows that preannouncement liquidity shocks, such as changes in the bid-ask spread and market depth, have significant predictive power for jumps. The predictive power is significant even after controlling for information shocks. Finally, we present evidence that post-jump order flow is less informative relative to the case where there is no jump at announcement.
Authors: George J. Jiang, Ingrid Lo, Adrien Verdelhan
Citations: 174
Financial Markets and Investment StrategiesFinancial Risk and Volatility ModelingMarket Dynamics and Volatility
ARXIV · 2010 · arXiv
Leverage is strongly related to liquidity in a market and lack of liquidity is considered a cause and/or consequence of the recent financial crisis. A repurchase agreement is a financial instrument where a security is sold simultaneously with an agreement to buy it back at a later date. Repurchase agreements (repos) market size is a very important element in calculating the overall leverage in a financial market. Therefore, studying the behavior of repos market size can help to understand a process that can contribute to the birth of a financial crisis. We hypothesize that herding behavior among large investors led to massive over-leveraging through the use of repos, resulting in a bubble (built up over the previous years) and subsequent crash in this market in early 2008. We use the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles and behavioral finance to study the dynamics of the repo market that led to the crash. The JLS model qualifies a bubble by the presence of characteristic patterns in the price dynamics, called log-periodic power law (LPPL) behavior. We show that there was significant LPPL behavior in the market before that crash and that the predicted range of times predicted by the model for the end of the bubble is consistent with the observations.
Authors: Wanfeng Yan, Ryan Woodard, Didier Sornette
Citations: N/A
repo marketq-fin
OPENALEX · 2010 · Quantitative Finance
We study model-driven statistical arbitrage in U.S. equities. The trading signals are generated in two ways: using Principal Component Analysis and using sector ETFs. In both cases, we consider the residuals, or idio-syncratic components of stock returns, and model them as mean-reverting processes. This leads naturally to “contrarian ” trading signals. The main contribution of the paper is the construction, back-testing and comparison of market-neutral PCA- and ETF- based strategies ap-plied to the broad universe of U.S. stocks. Back-testing shows that, af-ter accounting for transaction costs, PCA-based strategies have an av-erage annual Sharpe ratio of 1.44 over the period 1997 to 2007, with much stronger performances prior to 2003. During 2003-2007, the aver-age Sharpe ratio of PCA-based strategies was only 0.9. Strategies based on ETFs achieved a Sharpe ratio of 1.1 from 1997 to 2007, experiencing a similar degradation after 2002. We also introduce a method to account for daily trading volume infor-mation in the signals (which is akin to using “trading time ” as opposed to calendar time), and observe significant improvement in performance in the case of ETF-based signals. ETF strategies which use volume information achieve a Sharpe ratio of 1.51 from 2003 to 2007. The paper also relates the performance of mean-reversion statistical arbitrage strategies with the stock market cycle. In particular, we study in detail the performance of the strategies during the liquidity crisis of the summer of 2007. We obtain results which are consistent with Khandani and Lo (2007) and validate their “unwinding ” theory for the quant fund
Authors: Marco Avellaneda, Jeong-Hyun Lee
Citations: 340
Complex Systems and Time Series AnalysisFinancial Risk and Volatility ModelingStock Market Forecasting Methods
OPENALEX · 2009
Acknowledgments. Chapter 1 Introduction. Chapter 2 Evolution of High-Frequency Trading. Financial Markets And Technological Innovation. Evolution Of Trading Methodology. Chapter 3 Overview of the Business of High-Frequency Trading. Comparison With Traditional Approaches to Trading. Market Participants. Operating Model. Economics. Capitalizing a High-Frequency Trading Business. Conclusion. Chapter 4 Financial Markets Suitable for High-Frequency Trading. Financial Markets and Their Suitability for High-Frequency Trading. Conclusion. Chapter 5 Evaluating Performance of High-Frequency Strategies. Basic Return Characteristics. Comparative Ratios. Performance Attribution. Other Considerations in Strategy Evaluation. Conclusion. Chapter 6: Orders, Traders and their Applicability to High-Frequency Trading. Order Types. Order Distributions. Conclusion. Chapter 7: Market Inefficiency and Profit Opportunities at Different Frequencies. Predictability of Price Moves at High Frequencies. Conclusion. Chapter: 8: Searching for High-Frequency Trading Opportunities. Statistical Properties of Returns. Linear Econometric Models. Volatility Modeling. Nonlinear Models. Conclusion. Chapter 9: Working with Tick Data. Properties of Tick Data. Quantity and Quality of Tick Data. Bid-Ask Spreads. Bid-Ask Bounce. Modeling Arrivals of Tick Data. Applying Traditional Econometric Techniques to Tick Data. Conclusion. Chapter 10: Trading on Market Microstructure Inventory Models. Overview of Inventory Trading Strategies. Orders, Traders and Liquidity. Profitable Market Making. Directional Liquidity Provision. Conclusion. Chapter 11: Trading on Market Microstructure Information Models. Measures of Asymmetric Information. Information-Based Trading Models. Conclusion. Chapter 12: Event Arbitrage. Developing Event Arbitrage Trading Strategies. What Constitutes an Event? Forecasting Methodologies. Tradeable News. Application of Event Arbitrage. Conclusion. Chapter 13: Statistical Arbitrage in High Frequency Settings. Mathematical Foundations. Practical Applications of Statistical Arbitrage. Conclusion. Chapter 14: Creating and Managing Portfolios of High-Frequency Strategies. Analytical Foundations of Portfolio Optimization. Effective Portfolio Management Practices. Conclusion. Chapter 15: Back-Testing Trading Models. Evaluating Point Forecasts. Evaluating Directional Forecasts. Conclusion. Chapter 16: Implementing High-Frequency Trading Systems. Model Development Lifecycle. System Implementation. Testing Trading Systems. Conclusion. Chapter 17: Risk Management. Determining Risk Management Goals. Measuring Risk. Managing Risk. Conclusion. Chapter 18: Executing and Monitoring High-Frequency Trading. Executing High-Frequency Trading Systems. Monitoring High-Frequency Execution. Conclusion. Chapter 19: Post-Trade Profitability Analysis. Post-Trade Cost Analysis. Post-Trade Performance Analysis. References. About the Web Site. About The Author. Index.
OPENALEX · 2009 · Review of Financial Studies
This paper attempts to explain the credit default swap (CDS) premium, using a novel approach to identify the volatility and jump risks of individual firms from high-frequency equity prices. Our empirical results suggest that the volatility risk alone predicts 48% of the variation in CDS spread levels, whereas the jump risk alone forecasts 19%. After controlling for credit ratings, macroeconomic conditions, and firms' balance sheet information, we can explain 73% of the total variation. We calibrate a Merton-type structural model with stochastic volatility and jumps, which can help to match credit spreads after controlling for the historical default rates. Simulation evidence suggests that the high-frequency-based volatility measures can help to explain the credit spreads, above and beyond what is already captured by the true leverage ratio.
Authors: Benjamin Yibin Zhang, Hao Zhou, Haibin Zhu
Citations: 608
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyFinancial Distress and Bankruptcy Prediction
OPENALEX · 2008 · Applied Economics Letters
The desire of market participants to go long or short a portfolio of corporate credits led to the introduction of various types of indices of credit default swaps. In this article, we empirically investigate the relationships between the spreads of the North America CDX index and its tranches and their theoretical determinants. We find (1) support for a number of results predicted by the structural models used in credit risk modelling, such as the Merton model and (2) that CDX spreads are highly responsive to microstructure variables but not to macroeconomic variables.
Authors: Frank J. Fabozzi, Yichen Wang, Shih‐Kuo Yeh, Ren‐Raw Chen
Citations: 2
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyMonetary Policy and Economic Impact
OPENALEX · 2008 · Review of Financial Studies
We provide a model that links an asset's market liquidity (i.e., the ease with which it is traded) and traders' funding liquidity (i.e., the ease with which they can obtain funding). Traders provide market liquidity, and their ability to do so depends on their availability of funding. Conversely, traders' funding, i.e., their capital and margin requirements, depends on the assets' market liquidity. We show that, under certain conditions, margins are destabilizing and market liquidity and funding liquidity are mutually reinforcing, leading to liquidity spirals. The model explains the empirically documented features that market liquidity (i) can suddenly dry up, (ii) has commonality across securities, (iii) is related to volatility, (iv) is subject to “flight to quality,” and (v) co-moves with the market. The model provides new testable predictions, including that speculators' capital is a driver of market liquidity and risk premiums.
Authors: Markus K. Brunnermeier, Lasse Heje Pedersen
Citations: 4955
Financial Markets and Investment StrategiesBanking stability, regulation, efficiencyEconomic theories and models
OPENALEX · 2008 · Quantitative Finance
We study a stock dealer’s strategy for submitting bid and ask quotes in a limit order book. The agent faces an inventory risk due to the diffusive nature of the stock’s mid-price and a transactions risk due to a Poisson arrival of market buy and sell orders. After setting up the agent’s problem in a maximal expected utility framework, we derive the solution in a two step procedure. First, the dealer computes a personal indifference valuation for the stock, given his current inventory. Second, he calibrates his bid and ask quotes to the market’s limit order book. We compare this ”inventory-based ” strategy to a ”naive ” best bid/best ask strategy by simulating stock price paths and displaying the P&amp;L profiles of both strategies. We find that our strategy has a P&amp;L profile that has both a higher return and lower variance than the benchmark strategy. 1
Authors: Marco Avellaneda, Sasha Stoikov
Citations: 499
Financial Markets and Investment StrategiesComplex Systems and Time Series AnalysisStock Market Forecasting Methods
OPENALEX · 2008 · BIS quarterly review
As the financial crisis deepened and unsecured interbank markets effectively shut down, repo market activity became increasingly concentrated in the very shortest maturities and against the highest-quality collateral. Repo rates for US Treasury collateral fell relative to overnight index swap rates, while comparable sovereign repo rates in the euro area and the United Kingdom rose. The different dynamics across markets reflected, among other things, differences in the intensity of market disruptions and the extent of the scarcity of sovereign collateral.
Authors: Peter Hördahl, Michael R. King
Citations: 126
Banking stability, regulation, efficiencyCredit Risk and Financial RegulationsGlobal Financial Crisis and Policies
OPENALEX · 2007 · National Bureau of Economic Research
We study the nature of sovereign credit risk using an extensive sample of CDS spreads for 26 developed and emerging-market countries. Sovereign credit spreads are surprisingly highly correlated, with just three principal components accounting for more than 50 percent of their variation. Sovereign credit spreads are generally more related to the U.S. stock and high-yield bond markets, global risk premia, and capital flows than they are to their own local economic measures. We find that the excess returns from investing in sovereign credit are largely compensation for bearing global risk, and that there is little or no country-specific credit risk premium. A significant amount of the variation in sovereign credit returns can be forecast using U.S. equity, volatility, and bond market risk premia.
Authors: Francis A. Longstaff, Jun Pan, Lasse Heje Pedersen, Kenneth J. Singleton
Citations: 235
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyStochastic processes and financial applications
OPENALEX · 2007
Preface. Foreword. Acknowledgments. Chapter 1. Monte Carlo or Bust. Beginning. Whither? And Allusions. Chapter 2. Statistical Arbitrage. Introduction. Noise Models. Reverse Bets. Multiple Bets. Rule Calibration. Spread Margins for Trade Rules. Popcorn Process. Identifying Pairs. Refining Pair Selection. Event Analysis. Correlation Search in the Twenty-First Century. Portfolio Configuration and Risk Control. Exposure to Market Factors. Market Impact. Risk Control Using Event Correlations. Dynamics and Calibration. Evolutionary Operation: Single Parameter Illustration. Chapter 3. Structural Models. Introduction. Formal Forecast Functions. Exponentially Weighted Moving Average. Classical Time Series Models. Autoregression and Cointegration. Dynamic Linear Model. Volatility Modeling. Pattern Finding Techniques. Fractal Analysis. Which Return? A Factor Model. Factor Analysis. Defactored Returns. Prediction Model. Stochastic Resonance. Practical Matters. Doubling: A Deeper Perspective. Factor Analysis Primer. Prediction Model for Defactored Returns. Chapter 4. Law of Reversion. Introduction. Model and Result. The 75 percent Rule. Proof of the 75 percent Rule. Analytic Proof of the 75 percent Rule. Discrete Counter. Generalizations. Inhomogeneous Variances. Volatility Bursts. Numerical Illustration. First-Order Serial Correlation. Analytic Proof. Examples. Nonconstant Distributions. Applicability of the Result. Application to U.S. Bond Futures. Summary. Appendix 4.1: Looking Several Days Ahead. Chapter 5. Gauss is Not the God of Reversion. Introduction. Camels and Dromedaries. Dry River Flow. Some Bells Clang. Chapter 6. Interstock Volatility. Introduction. Theoretical Explanation. Theory versus Practice. Finish the Theory. Finish the Examples. Primer on Measuring Spread Volatility. Chapter 7. Quantifying Reversion Opportunities. Introduction. Reversion in a Stationary Random Process. Frequency of Reversionary Moves. Amount of Reversion. Movements from Quantiles Other Than the Median. Nonstationary Processes: Inhomogeneous Variance. Sequentially Structured Variances. Sequentially Unstructured Variances. Serial Correlation. Appendix 7.1: Details of the Lognormal Case in Example. Chapter 8. Nobel Difficulties. Introduction. Event Risk. Will Narrowing Spreads Guarantee Profits? Rise of a New Risk Factor. Redemption Tension. Supercharged Destruction. The Story of Regulation Fair Disclosure (FD). Correlation During Loss Episodes. Chapter 9. Trinity Troubles. Introduction. Decimalization. European Experience. Advocating the Devil. Stat. Arb. Arbed Away. Competition. Institutional Investors. Volatility Is the Key. Interest Rates and Volatility. Temporal Considerations. Truth in Fiction. A Litany of Bad Behavior. A Perspective on 2003. Realities of Structural Change. Recap. Chapter 10. Arise Black Boxes. Introduction. Modeling Expected Transaction Volume and Market Impact. Dynamic Updating. More Black Boxes. Market Deflation. Chapter 11. Statistical Arbitrage Rising. Catastrophe Process. Catastrophic Forecasts. Trend Change Identification. Using the Cuscore to Identify a Catastrophe. Is It Over? Catastrophe Theoretic Interpretation. Implications for Risk Management. Appendix 11.1: Understanding the Cuscore. Bibliography. Index.
OPENALEX · 2007 · National Bureau of Economic Research
Commodity futures risk premiums vary across commodities and over time depending on the level of physical inventories, as predicted by the Theory of Storage. Using a comprehensive dataset on 31 commodity futures and physical inventories between 1969 and 2006, we show that the convenience yield is a decreasing, non-linear relationship of inventories. Price measures, such as the futures basis, prior futures returns, and spot returns reflect the state of inventories and are informative about commodity futures risk premiums. The excess returns to Spot and Futures Momentum and Backwardation strategies stem in part from the selection of commodities when inventories are low. Positions of futures markets participants are correlated with prices and inventory signals, but we reject the Keynesian "hedging pressure" hypothesis that these positions are an important determinant of risk premiums.
Authors: Gary B. Gorton, Fumio Hayashi, K. Geert Rouwenhorst
Citations: 203
Market Dynamics and VolatilityRisk Management in Financial FirmsFinancial Markets and Investment Strategies
OPENALEX · 2007 · Journal of Applied Econometrics
Abstract A number of panel unit root tests that allow for cross‐section dependence have been proposed in the literature that use orthogonalization type procedures to asymptotically eliminate the cross‐dependence of the series before standard panel unit root tests are applied to the transformed series. In this paper we propose a simple alternative where the standard augmented Dickey–Fuller (ADF) regressions are augmented with the cross‐section averages of lagged levels and first‐differences of the individual series. New asymptotic results are obtained both for the individual cross‐sectionally augmented ADF (CADF) statistics and for their simple averages. It is shown that the individual CADF statistics are asymptotically similar and do not depend on the factor loadings. The limit distribution of the average CADF statistic is shown to exist and its critical values are tabulated. Small sample properties of the proposed test are investigated by Monte Carlo experiments. The proposed test is applied to a panel of 17 OECD real exchange rate series as well as to log real earnings of households in the PSID data. Copyright © 2007 John Wiley & Sons, Ltd.
Authors: M. Hashem Pesaran
Citations: 12224
Monetary Policy and Economic ImpactFiscal Policy and Economic GrowthEconomic Growth and Productivity
OPENALEX · 2007 · The Journal of Finance
ABSTRACT We find that liquidity is priced in corporate yield spreads. Using a battery of liquidity measures covering over 4,000 corporate bonds and spanning both investment grade and speculative categories, we find that more illiquid bonds earn higher yield spreads, and an improvement in liquidity causes a significant reduction in yield spreads. These results hold after controlling for common bond‐specific, firm‐specific, and macroeconomic variables, and are robust to issuers' fixed effect and potential endogeneity bias. Our findings justify the concern in the default risk literature that neither the level nor the dynamic of yield spreads can be fully explained by default risk determinants.
Authors: Long Chen, David A. Lesmond, Jason Zhanshun Wei
Citations: 1130
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyFinancial Markets and Investment Strategies
OPENALEX · 2006 · The Journal of Business
Recent evidence suggests that the parameters characterizing the implied volatility surface (IVS) in option prices are unstable. We study whether the resulting predictability patterns may be exploited. In a first stage we model the surface along cross-sectional moneyness and maturity dimensions. In a second stage we model the dynamics of the first-stage coefficients. We find that the movements of the S&P 500 IVS are highly predictable. Whereas profitable delta-hedged positions can be set up under selective trading rules, profits disappear when we increase transaction costs and trade on wide segments of the IVS.
Authors: Śılvia Gonçalves, Massimo Guidolin
Citations: 129
Stochastic processes and financial applicationsFinancial Markets and Investment StrategiesFinancial Risk and Volatility Modeling
OPENALEX · 2006 · Journal of Business and Economic Statistics
We study market microstructure noise in high-frequency data and analyze its implications for the realized variance (RV) under a general specification for the noise. We show that kernel-based estimators can unearth important characteristics of market microstructure noise and that a simple kernel-based estimator dominates the RV for the estimation of integrated variance (IV). An empirical analysis of the Dow Jones Industrial Average stocks reveals that market microstructure noise is time-dependent and correlated with increments in the efficient price. This has important implications for volatility estimation based on high-frequency data. Finally, we apply cointegration techniques to decompose transaction prices and bid–ask quotes into an estimate of the efficient price and noise. This framework enables us to study the dynamic effects on transaction prices and quotes caused by changes in the efficient price.
Authors: Peter Reinhard Hansen, Asger Lunde
Citations: 1224
Financial Risk and Volatility ModelingMarket Dynamics and VolatilityFinancial Markets and Investment Strategies
OPENALEX · 2006 · Review of Financial Studies
We test a Wall Street investment strategy, “pairs trading,” with daily data over 1962–2002. Stocks are matched into pairs with minimum distance between normalized historical prices. A simple trading rule yields average annualized excess returns of up to 11% for self-financing portfolios of pairs. The profits typically exceed conservative transaction-cost estimates. Bootstrap results suggest that the “pairs” effect differs from previously documented reversal profits. Robustness of the excess returns indicates that pairs trading profits from temporary mispricing of close substitutes. We link the profitability to the presence of a common factor in the returns, different from conventional risk measures.
Authors: Evan Gatev, William N. Goetzmann, K. Geert Rouwenhorst
Citations: 814
Financial Markets and Investment StrategiesCorporate Finance and GovernanceMonetary Policy and Economic Impact
OPENALEX · 2005 · Journal of Financial and Quantitative Analysis
Abstract This study employs a non-parametric approach to investigate the volatility risk premium in the over-the-counter currency option market. Using a large database of daily delta-neutral straddle quotes in four major currencies—the British pound, the euro, the Japanese yen, and the Swiss franc—we find that volatility risk is priced in all four currencies across different option maturities. We find that the volatility risk premium is negative, with the premium decreasing in maturity. Finally, we also find evidence that jump risk may be priced in the currency option market.
Authors: Buen Sin Low, Shaojun Zhang
Citations: 59
Stochastic processes and financial applicationsFinancial Risk and Volatility ModelingFinancial Markets and Investment Strategies
OPENALEX · 2005 · Quantitative Finance
‘Pairs Trading’ is an investment strategy used by many Hedge Funds. Consider two similar stocks which trade at some spread. If the spread widens short the high stock and buy the low stock. As the spread narrows again to some equilibrium value, a profit results. This paper provides an analytical framework for such an investment strategy. We propose a mean-reverting Gaussian Markov chain model for the spread which is observed in Gaussian noise. Predictions from the calibrated model are then compared with subsequent observations of the spread to determine appropriate investment decisions. The methodology has potential applications to generating wealth from any quantities in financial markets which are observed to be out of equilibrium.
Authors: Robert J. Elliott, John van der Hoek, W.P. Malcolm
Citations: 299
Complex Systems and Time Series AnalysisStock Market Forecasting MethodsFinancial Risk and Volatility Modeling
OPENALEX · 2005 · The Journal of Portfolio Management
There are two basic methodologies for portfolio optimization: tracking error variance (TEV) minimization (the industry standard for indexing), and a cointegration–optimal strategy (advocated by econometricians). Cointegration is a statistical tool that seeks to exploit a long–run equilibrium relationship between a portfolio and a benchmark, ensuring that the two are connected in the long term. For simple index tracking, the additional feature of cointegration is found to provide no clear advantages or disadvantages over TEV. Both models produce optimal portfolios that outperform a price–weighted benchmark during market crashes, assuming a long enough model calibration period. When tracking becomes more difficult, ensuring a cointegration relationship enhances performance. Cointegration–optimal portfolios dominate TEV equivalents for all the statistical arbitrage strategies based on enhanced indexation in all market circumstances.
Authors: Carol Alexander, Anca Dimitriu
Citations: 110
Monetary Policy and Economic ImpactFinancial Markets and Investment StrategiesFinancial Risk and Volatility Modeling
OPENALEX · 2005 · Review of Financial Studies
In theory, the sum of squares of log returns sampled at high frequency estimates their variance. When market microstructure noise is present but unaccounted for, however, we show that the optimal sampling frequency is finite and derives its closed-form expression. But even with optimal sampling, using say 5-min returns when transactions are recorded every second, a vast amount of data is discarded, in contradiction to basic statistical principles. We demonstrate that modeling the noise and using all the data is a better solution, even if one misspecifies the noise distribution. So the answer is: sample as often as possible.
Authors: Yacine Aı̈t-Sahalia, Per A. Mykland, Lan Zhang
Citations: 928
Financial Risk and Volatility ModelingComplex Systems and Time Series AnalysisStochastic processes and financial applications
OPENALEX · 2005 · European Finance Review
Abstract This paper examines the price differences between very liquid on-the-run U.S. Treasury securities and less liquid off-the-run securities over the on/off cycle. Comparing pairs of securities in time-series regressions allows us to disregard any fixed cross-sectional differences between securities. Also, since the liquidity of Treasury notes varies predictably over time, we can distinguish between current and future liquidity. We compare a variety of (microstructure-based) direct measures of liquidity to compare their effects on prices. We show that the liquidity premium depends primarily on the amount of remaining future liquidity.
Authors: David Goldreich, Bernd Hanke, Purnendu Nath
Citations: 189
Financial Markets and Investment StrategiesHousing Market and EconomicsMonetary Policy and Economic Impact
OPENALEX · 2004 · The Journal of Finance
ABSTRACT We examine the role of price discovery in the U.S. Treasury market through the empirical relationship between orderflow, liquidity, and the yield curve. We find that orderflow imbalances (excess buying or selling pressure) account for up to 26% of the day‐to‐day variation in yields on days without major macroeconomic announcements. The effect of orderflow on yields is permanent and strongest when liquidity is low. All of the evidence points toward an important role of price discovery in understanding the behavior of the yield curve.
Authors: Michael W. Brandt, Kenneth A. Kavajecz
Citations: 391
Financial Markets and Investment StrategiesMonetary Policy and Economic ImpactMarket Dynamics and Volatility
OPENALEX · 2004
Preface. Acknowledgments. PART ONE: BACKGROUND MATERIAL. Chapter 1. Introduction. The CAPM Model. Market Neutral Strategy. Pairs Trading. Outline. Audience. Chapter 2. Time Series. Overview. Autocorrelation. Time Series Models. Forecasting. Goodness of Fit versus Bias. Model Choice. Modeling Stock Prices. Chapter 3. Factor Models. Introduction. Arbitrage Pricing Theory. The Covariance Matrix. Application: Calculating the Risk on a Portfolio. Application: Calculation of Portfolio Beta. Application: Tracking Basket Design. Sensitivity Analysis. Chapter 4. Kalman Filtering. Introduction. The Kalman Filter. The Scalar Kalman Filter. Filtering the Random Walk. Application: Example with the Standard & Poor Index. PART TWO: STATISTICAL ARBITRAGE. Chapter 5. Overview. History. Motivation. Cointegration. Applying the Model. A Trading Strategy. Road Map for Strategy Design. Chapter 6. Pairs Selection in Equity Markets. Introduction. Common Trends Cointegration Model. Common Trends Model and APT. The Distance Measure. Interpreting the Distance Measure. Reconciling Theory and Practice. Chapter 7. Testing for Tradability. Introduction. The Linear Relationship. Estimating the Linear Relationship: The Multifactor Approach. Estimating the Linear Relationship: The Regression Approach. Testing Residual for Tradability. Chapter 8. Trading Design. Introduction. Band Design for White Noise. Spread Dynamics. Nonparametric Approach. Regularization. Tying Up Loose Ends. PART THREE: RISK ARBITRAGE PAIRS. Chapter 9. Risk Arbitrage Mechanics. Introduction. History. The Deal Process. Transaction Terms. The Deal Spread. Trading Strategy. Quantitative Aspects. Chapter 10. Trade Execution. Introduction. Specifying the Order. Verifying the Execution. Execution During the Pricing Period. Short Selling. Chapter 11. The Market Implied Merger Probability. Introduction. Implied Probabilities and Arrow-Debreu Theory. The Single-Step Model. The Multistep Model. Reconciling Theory and Practice. Risk Management. Chapter 12. Spread Inversion. Introduction. The Prediction Equation. The Observation Equation. Applying the Kalman Filter. Model Selection. Applications to Trading. Index.
Authors: Ganapathy Vidyamurthy
Citations: 360
Business Strategy and InnovationEconomic theories and models
OPENALEX · 2004 · Econstor (Econstor)
In 2004 all ECB publications will feature a motif taken from the €100 banknote. This paper can be downloaded from the ECB’s website
Authors: Lieven Baele, Annalisa Ferrando, Peter Hördahl, Elizaveta Krylova, Cyril Monnet
Citations: 535
Monetary Policy and Economic ImpactGlobal Financial Crisis and PoliciesEconomic Policies and Impacts
OPENALEX · 2004 · Journal of money credit and banking
Introduction 2. Monetary policy and equity markets: conceptual issues and data 1 3. Overall stock market reaction to monetary policy 1 4. Industry effects, the credit channel and Tobin's q 1 4.1 Industry-specific effects 1 4.2 Firm-specific effects 1 5. Propensity score matching 2 5.1 Algorithm of propensity score matching 2 5.2 Empirical results 2 6. Conclusions
Authors: Michael Ehrmann, Marcel Fratzscher
Citations: 379
Monetary Policy and Economic ImpactFinancial Markets and Investment StrategiesBanking stability, regulation, efficiency
OPENALEX · 2003 · Cambridge University Press eBooks
Proper conduct of monetary policy requires understanding the monetary transmission mechanism, to monitor the economy, make decisions on the stance of policy, and explain the policy actions to the public. Hence, gathering evidence on the monetary transmission mechanism in the euro area has been a priority for the Eurosystem. This 2003 book presents the results of a multi-year collaborative project conducted by the European Central Bank and the other Eurosystem central banks. First, macro data are consistently investigated with both VARs and structural models for the area as a whole and for individual countries. Second, the book contains an unprecedented set of studies on the effects of monetary policy using bank and firm panel data. The results described in country case studies and overview essays by central bank economists, along with a discussion chapter by eminent academics, provide an essential contribution to research on the subject.
Authors: Ignazio Angeloni, Kashyap, A. K., Mojon, Benoît, Eurosystem Monetary Transmission Network issuing body
Citations: 326
Global Financial Crisis and PoliciesBanking stability, regulation, efficiencyMonetary Policy and Economic Impact
OPENALEX · 2003 · The Journal of Derivatives
The accumulation of trading experience and empirical evidence since the original Black-Scholes (BS) model was developed, have made it increasingly evident that volatility is not a constant parameter, as BS assumed, but stochastic. With a second random factor associated with volatility affecting security returns, it would not be surprising if investors cared about bearing risk related to that factor. And there is considerable evidence from analysis of real world options that volatility risk is indeed a priced factor, with a negative price. That is, investors will pay extra for (accept lower returns on) securities like options whose values increase when volatility goes up. In that case, Black-Scholes implied volatilities will tend to be higher than actual volatilities, which provides one measure of the market price of volatility risk. Research on stochastic volatility has focused largely on stock index options, but modern portfolio theory makes a strong distinction between stochastic factors that are correlated with the market portfolio and those that are not, whose risk can be diversified. In this article, Bakshi and Kapadia look at the pricing of individual stock options to explore the effects of market volatility risk versus firm-specific volatility risk. They find that volatility risk is priced, negatively as expected, and that market volatility risk is more important than firm-specific risk.
Authors: Gurdip Bakshi, Nikunj Kapadia
Citations: 153
Financial Markets and Investment StrategiesStochastic processes and financial applicationsFinancial Risk and Volatility Modeling
OPENALEX · 2003 · Review of Financial Studies
This article introduces the concept of a statistical arbitrage opportunity (SAO). In a finite-horizon economy, a SAO is a zero-cost trading strategy for which (i) the expected payoff is positive, and (ii) the conditional expected payoff in each final state of the economy is nonnegative. Unlike a pure arbitrage opportunity, a SAO can have negative payoffs provided that the average payoff in each final state is nonnegative. If the pricing kernel in the economy is path independent, then no SAOs can exist. Furthermore, ruling out SAOs imposes a novel martingale-type restriction on the dynamics of securities prices. The important properties of the restriction are that it (1) is model-free, in the sense that it requires no parametric assumptions about the true equilibrium model, (2) can be tested in samples affected by selection biases, such as the peso problem, and (3) continues to hold when investors' beliefs are mistaken. The article argues that one can use the new restriction to empirically resolve the joint hypothesis problem present in the traditional tests of the efficient market hypothesis. Copyright 2003, Oxford University Press.
Authors: Oleg Bondarenko
Citations: 136
Financial Markets and Investment StrategiesStochastic processes and financial applicationsHousing Market and Economics
OPENALEX · 2003 · Review of Financial Studies
We investigate whether the volatility risk premium is negative by examining the statistical properties of delta-hedged option portfolios (buy the option and hedge with stock). Within a stochastic volatility framework, we demonstrate a correspondence between the sign and magnitude of the volatility risk premium and the mean delta-hedged portfolio returns. Using a sample of S&amp;P 500 index options, we provide empirical tests that have the following general results. First, the delta-hedged strategy underperforms zero. Second, the documented underperformance is less for options away from the money. Third, the underperformance is greater at times of higher volatility.Fourth, the volatility risk premium significantly affects delta-hedged gains even after accounting for jump-fears. Our evidence is supportive of a negative market volatility risk premium.
Authors: Gurdip Bakshi, Nikunj Kapadia
Citations: 1012
Financial Markets and Investment StrategiesStochastic processes and financial applicationsFinancial Risk and Volatility Modeling
OPENALEX · 2002
CONTENTS This book aims at providing a comprehensive treatment of alternative portfolio construction techniques ranging from traditional mean variance and lower partial moments based methods over Bayesian techniques to more recent developments as portfolio resampling or stochastic programming solutions using scenario optimization. 1. Traditional Portfolio Construction: Selected Issues - Starts with a review of Markowitz based solutions with a particular focus on issues that are of concern to practitioners but rarely treated in conventional textbooks. These involve asset liability management, clustering to redefine the investment universe, treatment of illiquid asset classes, life cycle investing, time varying covariances and implied return analysis. 2. Incorporating Deviations from Normality: Lower Partial Moments - Moves away from the classical model introducing non-normality. It will provide a toolkit to judge when non-normality is a problem and when it is not. Lower partial moment based portfolio construction is carefully discussed introducing all mathematical tools needed to optimally apply this important technique to real world portfolio problems. 3. Portfolio Resampling and Estimation Error - Introduces estimation error and to heuristically deal with it using either portfolio resampling or constrained optimization. A particular focus is given to the concept of resampled efficiency recently introduced into the literature as an increasing number of investors get interested into this particular form of dealing with estimation error. 4. Bayesian Analysis and Portfolio Choice - Deals with estimation error from a more conventional angle reviewing various Bayesian techniques. A special focus is given on data problems, particularly on to treat time series of different length as this is one of the main data problems faced by practitioners. 5. Scenario Optimisation - This chapter is a natural extension of all four previous chapters. It will describe the most general form of portfolio optimization that can simultaneously deal with data problems (estimation error, time series of different length) as well as with non-linear instruments, non-normal distributions and non-standard preferences. 6. Portfolio Construction with Transaction Costs deals with the most overlooked problem in practical portfolio construction: transaction costs. It shows various forms of transaction costs can be incorporated into the portfolio construction process. 7. Benchmark-Relative Optimisation - Leaves the world of asset allocation and reviews key concepts in making benchmark relative decisions. Again focus is given to problems rarely handled in traditional textbooks such as implicit funding assumptions and risk decomposition, multiple benchmark optimization, tracking error and its forecasting ability or tracking error efficiency versus mean variance efficiency. 8. Core-Satellite Investing: Budgeting Active Manager Risk - Concludes on budgeting active manager risk providing the mathematical tools to address questions like how much active? Where to be active? or Is core satellite investing superior to enhanced indexing?
OPENALEX · 2001 · The Journal of Finance
ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are highly cross‐correlated, and principal components analysis implies they are mostly driven by a single common factor. Although we consider several macroeconomic and financial variables as candidate proxies, we cannot explain this common systematic component. Our results suggest that monthly credit spread changes are principally driven by local supply/demand shocks that are independent of both credit‐risk factors and standard proxies for liquidity.
Authors: Pierre Collin-Dufresn, Robert S. Goldstein, J. Spencer Martin
Citations: 2183
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyFinancial Distress and Bankruptcy Prediction
OPENALEX · 2001 · The Journal of Finance
ABSTRACT Most structural models of default preclude the firm from altering its capital structure. In practice, firms adjust outstanding debt levels in response to changes in firm value, thus generating mean‐reverting leverage ratios. We propose a structural model of default with stochastic interest rates that captures this mean reversion. Our model generates credit spreads that are larger for low‐leverage firms, and less sensitive to changes in firm value, both of which are more consistent with empirical findings than predictions of extant models. Further, the term structure of credit spreads can be upward sloping for speculative‐grade debt, consistent with recent empirical findings.
Authors: Pierre Collin‐Dufresne, Robert S. Goldstein
Citations: 824
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyFinancial Distress and Bankruptcy Prediction
OPENALEX · 2001 · Econometrica
We study the implications of imperfect information for term structures of credit spreads on corporate bonds. We suppose that bond investors cannot observe the issuer’s assets directly, and receive instead only periodic and imperfect accounting reports. For a setting in which the assets of the firm are a geometric Brownian motion until informed equityholders optimally liquidate, we derive the conditional distribution of the assets, given accounting data and survivorship. Contrary to the perfect-information case, there exists a default-arrival intensity process. That intensity is calculated in terms of the conditional distribution of assets. Credit yield spreads are characterized in terms of accounting information. Generalizations are provided.
Authors: Darrell Duffie, David Lando
Citations: 1388
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyFinancial Distress and Bankruptcy Prediction
OPENALEX · 2001 · Econstor (Econstor)
This paper examines a comprehensive set of liquidity measures for the U.S. Treasury market. The measures are analyzed relative to one another, across securities, and over time. I find highly significant price impact coefficients, such that a simple model that explains price changes with net order flow produces an R² statistic above 30 percent for the two-year note. The price impact coefficients are highly correlated with bid-ask spreads and with episodes of reported poor liquidity (such as the fall 1998 financial markets turmoil). Quote and trade sizes correlate modestly with these episodes and with the other liquidity measures, as do yield spreads between on-the-run and off-the-run securities. In contrast, trading volume and trading frequency are only weakly correlated with these other measures, suggesting that they are poor liquidity proxies. The various measures are positively correlated across securities, almost without exception, especially for Treasury notes.
Authors: Michael J. Fleming
Citations: 125
Financial Markets and Investment StrategiesCredit Risk and Financial RegulationsStochastic processes and financial applications
OPENALEX · 2000 · American Economic Review
We study the monetary-transmission mechanism with a data set that includes quarterly observations of every insured U.S. commercial bank from 1976 to 1993. We find that the impact of monetary policy on lending is stronger for banks with less liquid balance sheets—i.e., banks with lower ratios of securities to assets. Moreover, this pattern is largely attributable to the smaller banks, those in the bottom 95 percent of the size distribution. Our results support the existence of a “bank lending channel” of monetary transmission, though they do not allow us to make precise statements about its quantitative importance. (JEL E44, E52, G32)
Authors: Anil Kashyap, Jeremy C. Stein
Citations: 2549
Banking stability, regulation, efficiencyHousing Market and EconomicsEconomic theories and models
OPENALEX · 2000 · The Journal of Finance
This paper characterizes all continuous price processes that are consistent with current option prices. This extends Derman and Kani (1994) , Dupire (1994 , 1997 ), and Rubinstein (1994) , who only consider processes with deterministic volatility. Our characterization implies a volatility forecast that does not require a specific model, only current option prices. We show how arbitrary volatility processes can be adjusted to fit current option prices exactly, just as interest rate processes can be adjusted to fit bond prices exactly. The procedure works with many volatility models, is fast to calibrate, and can price exotic options efficiently using familiar lattice techniques.
Authors: Mark Britten‐Jones, Anthony Neuberger
Citations: 1005
Stochastic processes and financial applicationsMonetary Policy and Economic ImpactEconomic theories and models
OPENALEX · 1999 · The Journal of Finance
The arrival of public information in the U.S. Treasury market sets off a two‐stage adjustment process for prices, trading volume, and bid‐ask spreads. In a brief first stage, the release of a major macroeconomic announcement induces a sharp and nearly instantaneous price change with a reduction in trading volume, demonstrating that price reactions to public information do not require trading. The spread widens dramatically at announcement, evidently driven by inventory control concerns. In a prolonged second stage, trading volume surges, price volatility persists, and spreads remain moderately wide as investors trade to reconcile residual differences in their private views.
Authors: Michael J. Fleming, Eli M. Remolona
Citations: 728
Financial Markets and Investment StrategiesMonetary Policy and Economic ImpactMarket Dynamics and Volatility
OPENALEX · 1998 · The Journal of Alternative Investments
MARK J. P. ANSON is affiliated with OppenheimerFunds, Inc., in New York. R ecent academic and practitioner Ž research Schneeweis 1996 ; . Schneeweis and Spurgin 1998 has emphasized the diversification benefits of a wide range of alternative investments including managed futures products as well as hedge funds. Many of these alternative investment products are based on active management strategies that often concentrate primarily on financial futures and options markets. Ž However, recent research Halpern and . Warsager 1998 has also focused on the diversification benefits of long-only investment strategies that focus on commodity futures in contrast to financial futures or options based contracts. There are several reasons why an investment in a commodity futures index may be an excellent source of portfolio diversification. First, commodity futures indexes invest only in nonfinancial commodity futures. This is important because the values of financial futures contracts, such as stock index futures or interest rate futures, are determined by the same economic fundamentals that determine the values of stocks and bonds. For instance, the values of these financial future contracts tend to be negatively influenced by the general level of interest rates and inflation rates. Conversely, commodity futures tend to be positively correlated with inflation. Second, commodity futures indexes are designed to be ‘‘long-only’’ investments. In other words, their investment in the commodity futures market is always positive: it is never short the market. As Edwards and Park 1996 noted, one of the issues with managed futures products is that they may hold both long and short speculative positions. Consequently, managed futures vehicles may not be sufficiently invested in the long-only commodities markets at the proper times to offer sufficient diversification benefits for stocks and bonds. The idea of commodity futures index investing is not new. An investible asset may be regarded as a separate asset class and a beneficial part of a diversified investor’s portfolio if they offer unique risk and return properties not easily obtainable with other investments. Under this definition, commodities also certainly qualify as an asset class. Ankrim and Hensel 1993 , Lummer and Siegel 1993 , and Kaplan and Lummer 1997 each concluded that an allocation to the Goldman Ž . Sachs Commodity Index GSCI provides a good diversifier for stocks and bonds within a mean-variance framework. Froot 1995 found that the GSCI is an effective portfolio diversification tool both as an initial hedge and as a secondary hedge after other real assets have already been
Authors: Mark J. P. Anson
Citations: 48
OPENALEX · 1998 · The Journal of Finance
ABSTRACT We propose a theory of securities market under‐ and overreactions based on two well‐known psychological biases: investor overconfidence about the precision of private information; and biased self‐attribution, which causes asymmetric shifts in investors' confidence as a function of their investment outcomes. We show that overconfidence implies negative long‐lag autocorrelations, excess volatility, and, when managerial actions are correlated with stock mispricing, public‐event‐based return predictability. Biased self‐attribution adds positive short‐lag autocorrelations (“momentum”), short‐run earnings “drift,” but negative correlation between future returns and long‐term past stock market and accounting performance. The theory also offers several untested implications and implications for corporate financial policy.
Authors: Kent Daniel, David Hirshleifer, Avanidhar Subrahmanyam
Citations: 5765
Financial Markets and Investment StrategiesAuditing, Earnings Management, GovernanceHousing Market and Economics
OPENALEX · 1996 · The Journal of Finance
ABSTRACT This article examines the optimal capital structure of a firm that can choose both the amount and maturity of its debt. Bankruptcy is determined endogenously rather than by the imposition of a positive net worth condition or by a cash flow constraint. The results extend Leland's (1994a) closed‐form results to a much richer class of possible debt structures and permit study of the optimal maturity of debt as well as the optimal amount of debt. The model predicts leverage, credit spreads, default rates, and writedowns, which accord quite closely with historical averages. While short term debt does not exploit tax benefits as completely as long term debt, it is more likely to provide incentive compatibility between debt holders and equity holders. Short term debt reduces or eliminates “asset substitution” agency costs. The tax advantage of debt must be balanced against bankruptcy and agency costs in determining the optimal maturity of the capital structure. The model predicts differently shaped term structures of credit spreads for different levels of risk. These term structures are similar to those found empirically by Sarig and Warga (1989). Our results have important implications for bond portfolio management. In general, Macaulay duration dramatically overstates true duration of risky debt, which may be negative for “junk” bonds. Furthermore, the “convexity” of bond prices can become “concavity.”
Authors: Hayne E. Leland, Klaus Bjerre Toft
Citations: 2067
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyCorporate Finance and Governance
OPENALEX · 1995 · The Journal of Economic Perspectives
The ‘credit channel’ theory of monetary policy transmission holds that informational frictions in credit markets worsen during tight-money periods. The resulting increase in the external finance premium--the difference in cost between internal and external funds--enhances the effects of monetary policy on the real economy. The authors document the responses of GDP and its components to monetary policy shocks and describe how the credit channel helps explain the facts. They discuss two main components of this mechanism, the balance sheet and bank lending channels. The authors argue that forecasting exercises using credit aggregates are not valid tests of this theory.
Authors: Ben Bernanke, Mark Gertler
Citations: 4129
Monetary Policy and Economic ImpactEconomic, financial, and policy analysisBanking stability, regulation, efficiency
OPENALEX · 1995
Foreword. 1. Markets and Market--Making. 2. Inventory Models. 3. Information--Based Models. 4. Strategic Trader Models I: Informed Traders. 5. Strategic Trader Models II: Uninformed Traders. 6. Information and the Price Process. 7. Market Viability and Stability. 8. Liquidity and the Relationships between Markets. 9. Issues in Market Performance.
Authors: Maureen O’Hara
Citations: 1352
Complex Systems and Time Series Analysis
OPENALEX · 1993 · IIE Transactions
This paper presents a model for analyzing inventory control policies for dealers that support the sales and service of manufactured goods. The environment faced by dealers is characterized by multiple stochastic demand classes (prioritized into emergency and regular), a principal source for boui emergency and regular requirements, multiple secondary sources for expedite requirements, and constraints on the lead time and service performance. We are concerned, in particular, with how dealers manage their inventory stockpiles and select expedite sources for replenishment when they have the option of prioritizing customers. The analysis is based on a periodic review, stochastic demand, (s,S) inventory model of dealer stock control. Exact Markov processes and approximate renewal based models are derived based on Cohen, Kleindorfer and Lee [4] and Cohen and Lee [5]. An approximation is used to develop a heuristic algorithm. Extensive numerical testing indicates that the algorithm generates solutions with acceptable cost and service penalties.
Authors: Ricardo Ernst, Morris A. Cohen
Citations: 12
Supply Chain and Inventory ManagementScheduling and Optimization Algorithms
OPENALEX · 1991 · Econometrica
This paper introduces an ARCH model (exponential ARCH) that (1) allows correlation between returns and volatility innovations (an important feature of stock market volatility changes), (2) eliminates the need for inequality constraints on parameters, and (3) allows for a straightforward interpretation of the "persistence" of shocks to volatility. In the above respects, it is an improvement over the widely-used GARCH model. The model is applied to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987. Copyright 1991 by The Econometric Society.
Authors: Daniel B. Nelson
Citations: 10436
Financial Risk and Volatility ModelingMonetary Policy and Economic ImpactMarket Dynamics and Volatility
SEMANTIC SCHOLAR · 2025 · Working papers
Pair trading remains a cornerstone strategy in quantitative finance, having consistently attracted scholarly attention from both economists and computer scientists. Over recent decades, research has expanded beyond traditional linear frameworks—such as regression- and cointegration-based models—to embrace advanced methodologies, including machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL). These techniques have demonstrated superior capacity to capture nonlinear dependencies and complex dynamics in financial data, thereby enhancing predictive performance and strategy design. Building on these academic developments, practitioners are increasingly deploying DL models to forecast asset price movements and volatility in equity and foreign exchange markets, leveraging the advantages of artificial intelligence (AI) for trading. In parallel, DRL has gained prominence in algorithmic trading, where agents can autonomously learn optimal trading policies by interacting with market environments, enabling systems that move beyond price prediction to dynamic signal generation and portfolio allocation. This paper provides a comprehensive survey of ML-, DL-, RL-, and DRL-based approaches to pair trading within quantitative finance. By systematically reviewing existing studies and highlighting their methodological contributions, it offers researchers a structured foundation for replication and further development. In addition, the paper outlines promising avenues for future research that extend the application of AI-driven methods in statistical arbitrage and market microstructure analysis.
Authors: Yufei Sun
Citations: 1
pairs trading