The Use of GARCH Models in VaR Estimation
AbstractWe 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.
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Bibliographic InfoPaper provided by University of Peloponnese, Department of Economics in its series Working Papers with number 0048.
Length: 34 pages
Date of creation: 2010
Date of revision:
Value at Risk; GARCH estimation; Backtesting; Volatility forecasting; Quantile Loss Function.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-03-28 (All new papers)
- NEP-ECM-2010-03-28 (Econometrics)
- NEP-ETS-2010-03-28 (Econometric Time Series)
- NEP-FOR-2010-03-28 (Forecasting)
- NEP-RMG-2010-03-28 (Risk Management)
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