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On the modeling assumptions of Historical Simulation for Value-at-Risk
Björn Löfdahl Grelsson
risk managementResource overview

On the modeling assumptions of Historical Simulation for Value-at-Risk

Paper2026

by Björn Löfdahl Grelsson

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risk management

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Suggested foundations for risk measurement and stress. 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.

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  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.

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