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GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series
Gilles Stupfler / Hibiki Kaibuchi / Yoshinori Kawasaki
extreme value theory / garchResource overview

GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series

Paper2022

by Gilles Stupfler, Hibiki Kaibuchi, Yoshinori Kawasaki

Tags
extreme value theorygarchrisk managementstatistical inference

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Suggested foundations for risk measurement and stress and volatility models and surfaces. 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. Stochastic Processes, Brownian Motion and Itô's Lemma

    Follow diffusion-based models and their assumptions.

  4. ARCH and GARCH

    Separate conditional volatility from observed returns.

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. Stochastic Volatility and the Heston Model

    Study a correlated stochastic-variance model.

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