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Paper / 2025SMQ / 193321865012479
Return-Decay Residuals and Tail-Risk Forecasting: Timing Artifacts, Conditional Inference, and Cross-Market Evidence
Chorok Lee
statistical inference / tail riskResource overview

Return-Decay Residuals and Tail-Risk Forecasting: Timing Artifacts, Conditional Inference, and Cross-Market Evidence

Paper2025

by Chorok Lee

Tags
statistical inferencetail risk

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Suggested foundations for risk measurement and stress and financial time series. 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. Stationarity and Unit Roots

    Check whether a relationship can be estimated consistently over time.

  4. Linear Regression

    Read the fitted relationship and its residuals.

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. Cointegration and Error Correction

    Study long-run relationships without confusing them with correlation.

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