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Given a universe of N assets, investors often form equally weighted portfolios (EWPs) by selecting subsets of assets. EWPs are simple, robust, and competitive out-of-sample, yet the uncertainty about which subset truly performs best is largely ignored. Traditional approaches typically rely on a single selected portfolio, but this fails to consider alternative investment strategies that may perform just as well when a…
Observing prices of European put and call options, we calibrate exponential Lévy models nonparametrically. We discuss the efficient implementation of the spectral estimation procedures for Lévy models of finite jump activity as well as for self-decomposable Lévy models. Based on finite sample variances, confidence intervals are constructed for the volatility, for the drift and, pointwise, for the jump density. As dem…
Stock market indices serve as fundamental market measurement that quantify systematic market dynamics. However, accurate index price prediction remains challenging, primarily because existing approaches treat indices as isolated time series and frame the prediction as a simple regression task. These methods fail to capture indices' inherent nature as aggregations of constituent stocks with complex, time-varying inter…
This study examined the endogenous nature of negative bubbles forming in meme stocks with the Log-Periodic Power Law (LPPL) Confidence Indicator (CI). A meme stock is a stock that has gained a significant amount of attention on a large social media platform such as Yahoo! or Reddit. This study examined four meme stocks including Tesla, Inc. (TSLA), GameStop Corp. (GME), Koss Corporation (KOSS), and AMC Entertainment …
Confidence intervals and joint confidence sets are constructed for the nonparametric calibration of exponential Lévy models based on prices of European options. To this end, we show joint asymptotic normality in the spectral calibration method for the estimators of the volatility, the drift, the jump intensity and the Lévy density at finitely many points.
In response to growing demand for resilient and transparent financial instruments, we introduce a novel framework for replicating private equity (PE) performance using liquid, AI-enhanced strategies. Despite historically delivering robust returns, private equity's inherent illiquidity and lack of transparency raise significant concerns regarding investor trust and systemic stability, particularly in periods of height…
Biondi et al. (2012) develop an analytical model to examine the emergent dynamic properties of share market price formation over time, capable to capture important stylized facts. These latter properties prove to be sensitive to regulatory regimes for fundamental information provision, as well as to market confidence conditions among actual and potential investors. Regimes based upon mark-to-market (fair value) measu…
If the probability of default parameters (PDs) fed as input into a credit portfolio model are estimated as through-the-cycle (TTC) PDs stressed market conditions have little impact on the results of the capital calculations conducted with the model. At first glance, this is totally different if the PDs are estimated as point-in-time (PIT) PDs. However, it can be argued that the reflection of stressed market condition…
The instability of historical risk factor correlations renders their use in estimating portfolio risk extremely questionable. In periods of market stress correlations of risk factors have a tendency to quickly go well beyond estimated values. For instance, in times of severe market stress, one would expect with certainty to see the correlation of yield levels and credit spreads go to -1, even though historical estima…
This paper presents PolySwarm, a novel multi-agent large language model (LLM) framework designed for real-time prediction market trading and latency arbitrage on decentralized platforms such as Polymarket. PolySwarm deploys a swarm of 50 diverse LLM personas that concurrently evaluate binary outcome markets, aggregating individual probability estimates through confidence-weighted Bayesian combination of swarm consens…
Robust optimization provides a principled framework for decision-making under uncertainty, with broad applications in finance, engineering, and operations research. In portfolio optimization, uncertainty in expected returns and covariances demands methods that mitigate estimation error, parameter instability, and model misspecification. Traditional approaches, including parametric, bootstrap-based, and Bayesian metho…
We study opportunistic optimal liquidation over fixed deadlines on BTC-USD limit-order books (LOB). We present RL-Exec, a PPO agent trained on historical replays augmented with endogenous transient impact (resilience), partial fills, maker/taker fees, and latency. The policy observes depth-20 LOB features plus microstructure indicators and acts under a sell-only inventory constraint to reach a residual target. Evalua…
Transaction costs and regime shifts are major reasons why paper portfolios fail in live trading. We introduce FR-LUX (Friction-aware, Regime-conditioned Learning under eXecution costs), a reinforcement learning framework that learns after-cost trading policies and remains robust across volatility-liquidity regimes. FR-LUX integrates three ingredients: (i) a microstructure-consistent execution model combining proporti…
Pricing extremely long-dated liabilities market consistently deals with the decline in liquidity of financial instruments on long maturities. The aim is to quantify the uncertainty of rates up to maturities of a century. We assume that the interest rates follow the affine mean-reverting Vasicek model. We model parameter uncertainty by Bayesian distributions over the parameters. The cross-sectional and time series par…
We will look at the entire cycle of the investment process relating to all aspects of, formulating an investment hypothesis, constructing a portfolio based on that, executing the trades to implement it, on-going risk management, periodically measuring the performance of the portfolio, and rebalancing the portfolio either due to an increase in the risk parameters or due to a deviation from the intended asset allocatio…
We present a reinforcement-learning (RL) framework for dynamic hedging of equity index option exposures under realistic transaction costs and position limits. We hedge a normalized option-implied equity exposure (one unit of underlying delta, offset via SPY) by trading the underlying index ETF, using the option surface and macro variables only as state information and not as a direct pricing engine. Building on the "…
Risk management is a prominent issue in peer-to-peer lending. An investor may naturally reduce his risk exposure by diversifying instead of putting all his money on one loan. In that case, an investor may want to minimize the Value-at-Risk (VaR) or Conditional Value-at-Risk (CVaR) of his loan portfolio. We propose a low degree of freedom deep neural network model, DeNN, as well as a high degree of freedom model, DSNN…
This research presents a framework for quantitative risk management in volatile markets, specifically focusing on expectile-based methodologies applied to the FTSE 100 index. Traditional risk measures such as Value-at-Risk (VaR) have demonstrated significant limitations during periods of market stress, as evidenced during the 2008 financial crisis and subsequent volatile periods. This study develops an advanced expec…
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.
Deposit outflow rate measures the pace at which deposits leave the banking system or individual banks, helping assess funding stability and confidence.
KBW Bank Index tracks the equity performance of major U.S. banks and provides insight into banking-sector health, credit transmission, and market confidence.
Gold price reflects demand for a non-yielding reserve asset and is often used as a signal for real yields, macro uncertainty, and confidence in fiat systems.
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.
The Triffin dilemma is the conflict of a reserve-currency issuer: the world needs the issuer to run liabilities (deficits) for reserve supply, but those deficits eventually undermine confidence in the reserve asset.
Consumer Confidence Index — Household expectations that influence spending, labor supply, and political pressure on policy.
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.
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.
Consumer Confidence Index — Household expectations that influence spending, labor supply, and political pressure on policy.
Deposit outflow rate measures the pace at which deposits leave the banking system or individual banks, helping assess funding stability and confidence.
Gold price reflects demand for a non-yielding reserve asset and is often used as a signal for real yields, macro uncertainty, and confidence in fiat systems.
KBW Bank Index tracks the equity performance of major U.S. banks and provides insight into banking-sector health, credit transmission, and market confidence.
The Triffin dilemma is the conflict of a reserve-currency issuer: the world needs the issuer to run liabilities (deficits) for reserve supply, but those deficits eventually undermine confidence in the reserve asset.