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Results for “IM” · papers 18 · wiki 36
Academic Papers · 18arXiv q-fin live 8 · desk corpus 1425
OpenAlex · Review of Financial Studies · 2005 · cites 933

How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise

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 t

Yacine Aı̈t-Sahalia, Per A. Mykland, Lan Zhang
OpenAlex · The Journal of Finance · 2004 · cites 390

Price Discovery in the U.S. Treasury Market: The Impact of Orderflow and Liquidity on the Yield Curve

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 i

Michael W. Brandt, Kenneth A. Kavajecz
OpenAlex · European Finance Review · 2005 · cites 189

The Price of Future Liquidity: Time-Varying Liquidity in the U.S. Treasury Market

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

David Goldreich, Bernd Hanke, Purnendu Nath
OpenAlex · Journal of Applied Econometrics · 2007 · cites 12575

A simple panel unit root test in the presence of cross‐section dependence

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 augmen

M. Hashem Pesaran
OpenAlex · The Journal of Finance · 2000 · cites 1011

Option Prices, Implied Price Processes, and Stochastic Volatility

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 adj

Mark Britten‐Jones, Anthony Neuberger
arXiv · arXiv · 2026

Deep Learning of Robust Market Making under Regime-Switching Order Flow

Classical market-making strategies based on stochastic control, such as the Avellaneda-Stoikov and the Guéant-Lehalle-Fernandez-Tapia (GLFT) extension, provide closed-form quoting rules, but rest on assumptions that break down at realistic microstructure timescales. One of them is that order flow is stationary, while empirical evidence points to the existence of regimes, possibly associated with algorithmic execution

Felipe Moret, Fabrizio Lillo
arXiv · arXiv · 2026

Regimes in the Order Flow

Financial markets alternate between periods of relative stability and instability, with structural breaks marking the transitions between these regimes. Identifying such breaks in real time is a central requirement for any trading or risk system operating at high frequency. This report studies Bayesian Online Changepoint Detection (BOCPD) and two extensions proposed in the literature, and applies them to the signed o

Ramzi Jebali
arXiv · arXiv · 2026

Optimal Block Time for AMM Liquidity Providers under Jump-Diffusion Prices

Loss-versus-Rebalancing (LVR) is the dominant adverse-selection cost borne by liquidity providers on automated market makers. Under geometric Brownian motion, arbitrage profit scales with the probability of a profitable block, which vanishes as the block time $Δt \to 0$; this is the standing argument for ever-shorter blocks. Modeling the reference price instead as a jump-diffusion, I show that the constant-product LV

Nils Bundi
arXiv · arXiv · 2026

Corporate Bond Yield Curve Modeling: A Rating-Based Regime-Switching Generalized CIR Approach

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 corporat

Maochun Xu, Yunqi Liang, Yi Hong
OpenAlex · Proceedings of the AAAI Conference on Artificial Intelligence · 2020 · cites 136

Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach

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 fi

Yang Liu, Qi Liu, Hongke Zhao, Pan Zhen, Chuanren Liu
arXiv · arXiv · 2024

Degree of Irrationality: Sentiment and Implied Volatility Surface

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 h

Jiahao Weng, Yan Xie
arXiv · arXiv · 2023

The implied volatility surface (also) is path-dependent

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

Hervé Andrès, Alexandre Boumezoued, Benjamin Jourdain
arXiv · arXiv · 2019

Forecasting security's volatility using low-frequency historical data, high-frequency historical data and option-implied volatility

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 tre

Huiling Yuan, Yong Zhou, Zhiyuan Zhang, Xiangyu Cui
arXiv · arXiv q-fin · 2017

Counterparty Trading Limits Revisited:CSAs, IM, SwapAgent(r), from PFE to PFL

The utility of Potential Future Exposure (PFE) for counterparty trading limits is being challenged by new market developments, notably widespread regulatory Initial Margin (using 99% 10-day exposure), and netting of trade and collateral flows. However PFE has pre-existing challenges w.r.t. portfolios/distributions, collateralization, netting set seniority, and overlaps with CVA. We introduce Potential Future Loss (PF

Chris Kenyon, Mourad Berrahoui, Benjamin Poncet
arXiv · arXiv q-fin · 2006

Topological Properties of the Minimal Spanning Tree in Korean and American Stock Markets

We investigate a factor that can affect the number of links of a specific stock in a network between stocks created by the minimal spanning tree (MST) method, by using individual stock data listed on the S&P500 and KOSPI. Among the common factors mentioned in the arbitrage pricing model (APM), widely acknowledged in the financial field, a representative market index is established as a possible factor. We found that

Cheoljun Eom, Gabjin Oh, Seunghwan Kim
OpenAlex · The Journal of Finance · 1996 · cites 2072

Optimal Capital Structure, Endogenous Bankruptcy, and the Term Structure of Credit Spreads

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

Hayne E. Leland, Klaus Bjerre Toft
OpenAlex · Review of International Political Economy · 2016 · cites 276

The (impossible) repo trinity: the political economy of repo markets

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 stabilit

Daniela Gabor
Wiki Entities · 36
AI Systems

Adam Optimizer

Adam is an adaptive first-order optimizer that keeps exponential moving averages of the gradient and its square, giving per-parameter step sizes.

AI Systems

CLIP

CLIP jointly trains an image encoder and a text encoder so matched image–caption pairs are close in a shared space, enabling zero-shot visual classification by text prompts.

AI Systems

Contrastive Learning

Contrastive learning pulls representations of related pairs together and pushes unrelated pairs apart. It is the pretraining idea behind SimCLR, CLIP, and many embedding models.

AI Systems

Deep Q-Network

DQN approximates Q(s, a) with a deep net, using experience replay and a frozen target network so the TD target does not chase itself every step.

AI Systems

Dropout

Dropout randomly zeroes hidden units during training so the net cannot rely on any single co-adaptation, then scales weights at test time (or uses inverted dropout).

AI Systems

Early Stopping

Early stopping treats training time as a capacity knob: halt when a validation metric stops improving so the model does not wander into overfit.

AI Systems

Embedding

An embedding is a learned dense vector for an object (token, sentence, image, user) such that geometry supports retrieval, clustering, or as input to a downstream model.

AI Systems

Generative Adversarial Network

A GAN trains a generator and a discriminator against each other: the generator maps noise to fake samples, the discriminator learns real vs fake, and the equilibrium is a generator whose samples match the data distribution.

AI Systems

Gradient Descent

Gradient descent updates parameters against the gradient of a loss: θ ← θ − η ∇_θ L. Stochastic and mini-batch variants make the method tractable on large datasets.

AI Systems

In-Context Learning

In-context learning is when a frozen language model improves at a task from examples placed in the prompt, without weight updates.

AI Systems

Learning Rate Schedule

A learning-rate schedule is the planned path of η_t — warmup, cosine, step decay — that often matters more than the architecture headline on a given run.

AI Systems

Neural Network

A neural network is a layered function approximator: units compute a weighted sum, apply a nonlinearity, and pass the result forward so the whole stack can learn a mapping from inputs to outputs.

AI Systems

Policy Gradient

Policy gradient methods optimize a parameterized policy π_θ directly by ascending the gradient of expected return, rather than via an action-value table.

AI Systems

Proximal Policy Optimization

PPO is a policy-gradient algorithm that clips the probability ratio so each update stays close to the previous policy, giving much of TRPO’s stability with first-order SGD.

AI Systems

Q-Learning

Q-learning is an off-policy TD method that learns action values Q(s, a) toward the greedy target r + γ max_a′ Q(s′, a′), without needing the behavior policy to be optimal.

AI Systems

Recurrent Neural Network

An RNN applies the same transition to a sequence, threading a hidden state through time: h_t = f(h_{t−1}, x_t). Plain RNNs struggle to learn long dependencies.

AI Systems

Regularization

Regularization is any constraint that trades train fit for expected live error: weight decay, dropout, early stopping, data augmentation, or a simpler hypothesis class.

AI Systems

Reinforcement Learning

Reinforcement learning trains a policy to maximize expected return by interacting with an environment: states, actions, rewards, and (usually) a discount factor.

AI Systems

Reinforcement Learning from Human Feedback

RLHF fits a reward model to human (or AI) preference comparisons, then optimizes a language model against that reward, usually with a KL penalty back to a reference policy.

AI Systems

Softmax

Softmax maps a real vector to a probability simplex: softmax(z)_i = exp(z_i) / Σ exp(z_j). It is the standard last layer for classification and attention weights.

AI Systems

Variational Autoencoder

A VAE is a probabilistic autoencoder: the encoder outputs a distribution q(z|x), the decoder p(x|z), and training maximizes an ELBO with a KL term that keeps the latent well-behaved.

Banking

Balance Sheet Constraint Dealer

Balance Sheet Constraint Dealer — Dealer SLR/balance-sheet limits reducing intermediation.

Banking

Bank CDS Index

Bank CDS Index tracks the cost of insuring major bank credit risk and serves as a real-time indicator of banking-system stress and confidence.

Commodities

Crude Oil Contango

Crude Oil Contango — Upward-sloping futures curve implying storage economics and weak spot demand.

Credit

Bankruptcy

Bankruptcy is a court process that stays creditors and restructures or liquidates claims when a firm cannot meet its obligations as they come due.

Credit

CDX IG Index

CDX IG Index tracks the cost of insuring a basket of North American investment-grade corporate credit and is widely used as a real-time gauge of credit stress and financial conditions.

Credit

Chapter 11

Chapter 11 is US reorganization bankruptcy — the firm tries to stay a going concern while claims are rewritten.

Credit

Chapter 7

Chapter 7 is US liquidation bankruptcy — a trustee sells assets and pays claims in priority; the going concern is over.

Credit

Collateralized Debt Obligation

A CDO is a securitization of debt (or of other securitizations) into tranches — correlation and a waterfall, not a simple bond.

Credit

Debt Covenant

A debt covenant is a contractual limit on the borrower — maintain a ratio, not do a thing, or report a thing — that turns a miss into a default or a fee.

Credit

Interest Coverage Ratio

Interest coverage is EBIT (or EBITDA) divided by interest expense — how many times operating profit can pay the coupon bill.

Credit

Recovery Rate

Recovery is what a claim is worth after default, as a fraction of par — the complement of loss given default.

Crypto

Bitcoin Difficulty Adjustment

Bitcoin Difficulty Adjustment — Periodic retarget of mining difficulty to stabilize block times.

Crypto

Crypto Realized Vol Regime

Crypto Realized Vol Regime — Shifts in realized volatility that redefine sizing and carry.

Crypto

Stablecoin

A stablecoin is a token that targets a peg, usually $1 — a money-market claim or an algorithmic hope, depending on the reserves.

CTA

CTA Capacity and Market Limits

How much money a program can run before it is the market — position limits, ADV caps, and the point where adding AUM only buys slippage.

Option Blackboard · 3
Encyclopedia · 24
AI Systems · Foundations

Adam Optimizer

Adam is an adaptive first-order optimizer that keeps exponential moving averages of the gradient and its square, giving per-parameter step sizes.

Strategies · Foundations

Analyst Revision Strategy

Long names with upward earnings-estimate revisions and short downward revisions — the revision-momentum book.

Desk Slang · Foundations

Animal Spirits

Animal spirits is Keynes’s name for the non-model confidence that makes people invest or refuse to — the residual when rates and cash flows are not enough to explain the tape.

Financial Crises · Foundations

Archegos 2021

Archegos was a family-office total-return-swap blow-up in March 2021: concentrated longs, huge hidden leverage across prime brokers, and a week of block sales that hit ViacomCBS and others.

Banking · Foundations

Balance Sheet Constraint Dealer

Balance Sheet Constraint Dealer — Dealer SLR/balance-sheet limits reducing intermediation.

Banking · Foundations

Bank CDS Index

Bank CDS Index tracks the cost of insuring major bank credit risk and serves as a real-time indicator of banking-system stress and confidence.

Credit · Foundations

Bankruptcy

Bankruptcy is a court process that stays creditors and restructures or liquidates claims when a firm cannot meet its obligations as they come due.

Mathematics · Foundations

Bayesian Inference

Bayesian inference updates a prior distribution over parameters with data via Bayes’ rule to get a posterior — beliefs as probabilities, not just a point estimate.

Crypto · Foundations

Bitcoin Difficulty Adjustment

Bitcoin Difficulty Adjustment — Periodic retarget of mining difficulty to stabilize block times.

Equity · Foundations

Blue Chip

A blue chip is a large, established, usually profitable listed company — a reputation, not a risk-free claim.

Mathematics · Foundations

Brownian Motion

Brownian motion (Wiener process) is the continuous-time random walk with independent Gaussian increments — the backbone of Black–Scholes, Ito calculus, and most diffusion models.

Equity · Foundations

Bull Market

A bull market is a sustained rise in a broad price index — a regime label, not a law, usually tagged after a ~20% rally from a low.

Quant · Foundations

Capital Asset Pricing Model

CAPM says expected excess return is beta times the market risk premium — one factor, one line, many violations.

Credit · Foundations

CDX IG Index

CDX IG Index tracks the cost of insuring a basket of North American investment-grade corporate credit and is widely used as a real-time gauge of credit stress and financial conditions.

Mathematics · Foundations

Central Limit Theorem

The central limit theorem says that sums of many independent, finite-variance shocks look Gaussian — which is why so many models start with a normal, and why they fail when those assumptions fail.

Credit · Foundations

Chapter 11

Chapter 11 is US reorganization bankruptcy — the firm tries to stay a going concern while claims are rewritten.

Credit · Foundations

Chapter 7

Chapter 7 is US liquidation bankruptcy — a trustee sells assets and pays claims in priority; the going concern is over.

Fixed Income · Foundations

Cheapest to Deliver

Cheapest-to-deliver is the bond a futures (or CDS) seller will deliver because it minimizes their cost — the option that sits inside the contract.

Economy · Foundations

China Credit Impulse

China credit impulse measures the change in new credit growth relative to GDP and is widely used as a leading indicator for Chinese demand and global cyclical momentum.

Emerging Markets · Foundations

China Property Cycle

China Property Cycle — Developer stress and land sales impacting global commodities and EM growth.

AI Systems · Foundations

CLIP

CLIP jointly trains an image encoder and a text encoder so matched image–caption pairs are close in a shared space, enabling zero-shot visual classification by text prompts.

Fixed Income · Foundations

CLO Issuance

CLO Issuance — Structured credit supply that absorbs leveraged loans and shapes spread regimes.

Credit · Foundations

Collateralized Debt Obligation

A CDO is a securitization of debt (or of other securitizations) into tranches — correlation and a waterfall, not a simple bond.

AI Systems · Foundations

Contrastive Learning

Contrastive learning pulls representations of related pairs together and pushes unrelated pairs apart. It is the pretraining idea behind SimCLR, CLIP, and many embedding models.

Cards · 6
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