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Tail-Safe Hedging: Explainable Risk-Sensitive Reinforcement Learning with a White-Box CBF--QP Safety Layer in Arbitrage-Free Markets
Jian'an Zhang
financial engineering / optimizationResource overview

Tail-Safe Hedging: Explainable Risk-Sensitive Reinforcement Learning with a White-Box CBF--QP Safety Layer in Arbitrage-Free Markets

Paper2025

by Jian'an Zhang

Tags
financial engineeringoptimizationreinforcement learningrisk managementtail risk

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Understand this paper

Suggested foundations for risk measurement and stress and optimisation and numerical methods. Specialist prerequisites vary by paper.

Before you read

  1. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  2. Expected Shortfall and Other Tail Risk Measures

    Distinguish a loss quantile from the average loss in the tail.

  3. Vectors, Matrices and Linear Systems

    Read the linear algebra used to define a numerical problem.

  4. Constrained and Convex Optimisation

    Identify the objective, constraints and conditions for an optimum.

Go further

  1. VaR Estimation in Practice: Variance-Covariance, Historical Simulation, Monte Carlo

    Compare risk estimators and their assumptions.

  2. Backtesting VaR and ES: Coverage Tests and Model Validation

    Check forecasts against realised losses.

  3. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

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Explore Quantitative Risk Management Explore Linear Algebra, Optimisation and Numerical Methods

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