20 papers
This paper challenges the conventional wisdom that diversification always reduces risk by demonstrating that for heavy-tailed losses with infinite means, a diversified portfolio can stochastically dominate a "one-basket" benchmark, thereby increasing the probability of exceeding any given loss threshold.
This paper proposes a continuous-time stochastic extension of the Gordon-Loeb model that incorporates Hawkes processes to capture attack clustering, demonstrating through dynamic programming that accounting for such clustering yields more responsive and effective cybersecurity investment policies compared to traditional static or Poisson-based approaches.
This paper derives explicit solutions for unconstrained and counter-monotonic inf-convolutions among agents with heterogeneous distortion risk measures, including risk-seeking preferences, showing that these optimal allocations can be represented by a generalization of distortion risk measures.
This paper presents a unified framework that reconstructs the multilayer bank-firm network structure of the Italian economy from balance sheet data to simulate shock propagation and identify systemic risks, enabling detailed stress testing without relying on inaccessible, privacy-protected network information.
This paper introduces Slippage-at-Risk (SaR), a forward-looking quantitative framework that utilizes current order book microstructure to measure liquidity risk, optimize capital requirements, and predict systemic stress in perpetual futures exchanges, as validated by empirical analysis of the October 2025 Hyperliquid liquidation cascade.
This paper establishes a non-standard analysis framework that unifies coherent risk measures and their finite-sample estimators through hyperfinite representations, yielding discrete Kusuoka formulae, plug-in consistency results, and asymptotic normality via a transparent probability-to-statistics dictionary.
This paper introduces an ensemble Gaussian Process Regression method that significantly reduces computational complexity while outperforming existing machine learning models in predicting US stock returns and constructing superior mean-variance optimal portfolios based on prediction uncertainty.
This paper proposes Generative Adversarial Regression (GAR), a minimax framework that learns conditional risk scenarios by training generators to align their policy-induced risk with real data across a broad class of policies, thereby outperforming existing baselines in preserving downstream risk metrics like Value-at-Risk and Expected Shortfall.
This paper introduces Calibrated Credit Intelligence (CCI), a deployment-oriented framework that integrates Bayesian uncertainty quantification, fairness-constrained gradient boosting, and shift-aware fusion to deliver accurate, reliable, and equitable credit risk scores that remain robust under temporal distribution shifts.
This paper proposes a two-tier stochastic control model for market making in aggregator-routed RFQ markets that links dealer quoting strategies to slowly varying win-score promotion gates, revealing how the interplay between fast inventory dynamics and slow score evolution can lead to bistability, hysteresis, and endogenous "campaign vs. harvest" quoting patterns.
This paper introduces the Weighted Generalized Risk Measure and its associated Weighted Risk Quadrangle framework to synthesize heterogeneous risk assessments, providing analytical characterizations, tractable optimization methods, and empirical evidence of superior portfolio performance in mitigating losses from single-scenario judgments.
This paper proposes a hybrid Hidden Markov Model that combines Laplace quantile-defined market states with a Poisson-driven jump-duration mechanism to generate synthetic equity excess growth rates that simultaneously preserve heavy-tailed distributions, volatility clustering, and realistic tail-state dwell times, outperforming standard GARCH and HMM models in joint distributional and temporal fidelity.
This paper introduces a novel class of single- and multi-level Fourier-RQMC algorithms that leverage frequency-domain integration and randomized quasi-Monte Carlo sampling to achieve superior accuracy and computational efficiency in estimating multivariate shortfall risk and optimal capital allocations compared to traditional Monte Carlo methods.
This paper establishes robust upper and lower bounds for life insurance functionals under two distinct mortality assumptions—one requiring almost sure consistency and the other allowing for expected deviations with life table constraints—to quantify the impact of fractional age uncertainty on contract valuations without relying on specific parametric models.
This paper proposes a dynamic market-making framework for spot FX that jointly optimizes size-dependent quotes and trade rejection decisions to manage latency-driven adverse selection, while incorporating an endogenous feedback mechanism where past rejections modulate client order flow, ultimately solved via dynamic programming and approximated by a tractable adiabatic-quadratic method.
This paper investigates the relationship between long-only global minimum variance portfolios and asset factor exposures, providing explicit analytical solutions for single-factor models and geometric descriptions for multi-factor scenarios, validated through empirical US stock data.
This paper introduces two shortfall-aware reinforcement learning frameworks, RLOP and QLBS, which outperform traditional parametric models in reducing tail risk and hedging shortfalls for SPY and XOP options, thereby offering a more robust approach to autonomous derivatives risk management.
This paper proposes a tractable stochastic correlation extension of the Vasicek credit risk model using circular diffusion processes to capture time-varying dependence, thereby deriving closed-form expressions for joint default probabilities and demonstrating how correlation risk significantly impacts tail-event assessments through empirical analysis of U.S. bank charge-off rates.
This paper proposes a practical framework for analyzing extreme values in finite, multivariate, and correlated systems—demonstrated through high-frequency finance data—by rotating returns into the correlation matrix's eigenbasis to isolate collective and idiosyncratic effects, thereby enabling the use of univariate peaks-over-threshold methods to estimate tail risks while accounting for nonstationarity.
This paper introduces an axiomatic framework for allocation mechanisms in decentralized exchange markets that account for frictional transfer costs, characterizing robust linear mechanisms and "Robust Conditional Mean" mechanisms while linking them to literature on decentralized risk sharing.