The Use of GARCH Models in VaR Estimation

Timotheos Angelidis, Alexandros Benos and Stavros Degiannakis

MPRA Paper from University Library of Munich, Germany

Abstract: We evaluate the performance of an extensive family of ARCH models in modelling daily Valueat-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.

Keywords: Value at Risk; GARCH estimation; Backtesting; Volatility forecasting; Quantile Loss Function (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 G15 (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (115)

Published in Statistical Methodology 1.2(2004): pp. 105-128

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Related works:
Working Paper: The Use of GARCH Models in VaR Estimation (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:96332

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