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The Use of GARCH Models in VaR Estimation

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  • Timotheos Angelidis
  • Alexandros Benos
  • Stavros Degiannakis

Abstract

We evaluate the performance of an extensive family of ARCH models in modelling daily Value-at-Risk (VaR) of perfectly diversified portfolios in five stock indices, using a number of distributional assumptions and sample sizes. We find, first, that leptokurtic distributions are able to produce better one-step-ahead VaR forecasts; second, the choice of sample size is important for the accuracy of the forecast, whereas the specification of the conditional mean is indifferent. Finally, the ARCH structure producing the most accurate forecasts is different for every portfolio and specific to each equity index.

Suggested Citation

  • Timotheos Angelidis & Alexandros Benos & Stavros Degiannakis, 2010. "The Use of GARCH Models in VaR Estimation," Working Papers 0048, University of Peloponnese, Department of Economics.
    • Handle: RePEc:uop:wpaper:0048
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    More about this item

    Keywords

    Value at Risk; GARCH estimation; Backtesting; Volatility forecasting; Quantile Loss Function.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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