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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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4 results for “In-CVaR”

Statistical Robustness of Interval CVaR Based Regression Models under Perturbation and Contaminationmath.OC

Study enhances robustness of In-CVaR based regression models under perturbation and contamination.

problem Enhancing robustness of nonlinear regression models under perturbation and contamination.

method Introduces interval conditional value-at-risk (In-CVaR) and rigorously analyzes its robustness properties under both perturbation and contamination.

result The In-CVaR based estimator is qualitatively robust in terms of the Prokhorov metric if and only if the largest portion of losses is trimmed.

robust regression In-CVaR interval CVaR nonlinear regression contamination

2026-01-16abs ↗pdf ↗

Bias-Corrected Peaks-Over-Threshold Estimation of the CVaRstat.ME

New method corrects bias in CVaR estimation for extreme risks.

problem Limited data above VaR leads to poor CVaR estimation.

method Bias-corrected peaks-over-threshold (POT) estimation using GPD.

result Asymptotically unbiased CVaR estimator with lower threshold.

CVaR bias correction extreme risk GPD POT

2021-03-08abs ↗pdf ↗

Uncertainty-Aware Deep Hedgingq-fin.CP

Improved deep hedging with ensemble uncertainty quantification.

problem Uncertainty in deep hedging models hinders their deployment.

method Trained an ensemble of LSTM networks to quantify uncertainty in deep hedging under Heston volatility and proportional transaction costs.

result The ensemble's disagreement provides a strong predictive confidence measure for hedge performance.

deep hedging ensemble learning uncertainty quantification Heston model CVaR optimization

2026-03-10abs ↗pdf ↗

Provably Efficient CVaR RL in Low-rank MDPscs.LG

New RL algorithm maximizes CVaR in low-rank MDPs with provable efficiency.

problem Maximizing CVaR in large state spaces with function approximation.

method Upper Confidence Bound (UCB) bonus-driven algorithm for low-rank MDPs.

result Achieves sample complexity of O(H^7 A^2 d^4 / τ^2 ε^2) for ε-optimal CVaR.

Reinforcement Learning Conditional Value at Risk Low-rank MDPs Upper Confidence Bound Function Approximation

2023-11-20abs ↗pdf ↗