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Risk Management of Risk Under the Basel Accord: A Bayesian Approach to Forecasting Value-at-Risk of VIX Futures
Casarin, R. / Chang, C-L. / Jiménez-Martín, J.A. / McAleer, M.J. / Pérez-Amaral, T.
bayesian statistics / derivativesResource overview

Risk Management of Risk Under the Basel Accord: A Bayesian Approach to Forecasting Value-at-Risk of VIX Futures

Paper2011

by Casarin, R., Chang, C-L., Jiménez-Martín, J.A., McAleer, M.J., Pérez-Amaral, T.

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bayesian statisticsderivativesrisk management

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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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Casarin, R., Chang, C-L., Jiménez-Martín, J.A., McAleer, M.J., Pérez-Amaral, T.

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