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On data-driven robust distortion risk measures for non-negative risks with partial information
Linxiao Wei / Ran Wang / Xiangyu Han / Yijun Hu
optimizationResource overview

On data-driven robust distortion risk measures for non-negative risks with partial information

Paper2025

by Linxiao Wei, Ran Wang, Xiangyu Han, Yijun Hu

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optimization

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

Before you read

  1. Vectors, Matrices and Linear Systems

    Read the linear algebra used to define a numerical problem.

  2. Constrained and Convex Optimisation

    Identify the objective, constraints and conditions for an optimum.

  3. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  4. Expected Shortfall and Other Tail Risk Measures

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

Go further

  1. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

  2. Numerical Conditioning and Stability

    Separate model error from unstable numerical computation.

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

    Compare risk estimators and their assumptions.

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