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ESG as Priced Crash Insurance: State-Dependent Tail Risk and Deconfounding Evidence
Jiayu Yi / Minxuan Hu / Wenxi Sun / Ziheng Chen
causal inference / equity marketsResource overview

ESG as Priced Crash Insurance: State-Dependent Tail Risk and Deconfounding Evidence

Paper2026

by Jiayu Yi, Minxuan Hu, Wenxi Sun, Ziheng Chen

Tags
causal inferenceequity marketsesgmachine learningtail risk

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Suggested foundations for risk measurement and stress and asset pricing and factor research. 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. Linear Regression

    Interpret exposures, residuals and uncertainty in fitted coefficients.

  4. Financial Return Characteristics

    Work with returns and recognise their empirical limitations.

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. Factor construction

    Connect factor definitions to portfolio construction.

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