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EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning
Jacky Chen / Konstantinos N. Plataniotis / Parvin Malekzadeh / Zeyu Wang / Zissis Poulos
derivatives / extreme value theoryResource overview

EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning

Paper2024

by Jacky Chen, Konstantinos N. Plataniotis, Parvin Malekzadeh, Zeyu Wang, Zissis Poulos

Tags
derivativesextreme value theorymachine learningoptions hedgingreinforcement learningrisk managementtail risk

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Suggested foundations for risk measurement and stress and options and derivatives pricing. 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. The Binomial Model

    Build replication and risk-neutral probabilities in a tractable model.

  4. Market Model and Risk-Neutral Valuation

    Distinguish pricing probabilities from physical forecasts.

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. The Black-Scholes PDE, Formula and Implied Volatility

    Connect the pricing equation, closed-form value and implied volatility.

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