Empirical analysis of GARCH models in value at risk estimation |
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| Institution: | 1. Universität zu Köln and Technische Universität Dortmund, Meister-Ekkehart-Str. 9, 50923 Köln, Germany;2. Universität Leipzig and Technische Universität Dortmund, Grimmaische Str. 12, 04109 Leipzig, Germany;3. FOM Hochschule für Oekonomie & Management, Feldstraße 88, 46535 Dinslaken, Germany;1. Department of Economics, Erasmus University Rotterdam, P.O. Box 1738, 3000DR, The Netherlands;2. Economics and Research Division, De Nederlandsche Bank, P.O. Box 98, 1000AB, The Netherlands;1. Rawls College of Business, Texas Tech University, Lubbock, TX, United States;2. College of Business, Zayed University, Dubai, United Arab Emirates |
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| Abstract: | This paper studies seven GARCH models, including RiskMetrics and two long memory GARCH models, in Value at Risk (VaR) estimation. Both long and short positions of investment were considered. The seven models were applied to 12 market indices and four foreign exchange rates to assess each model in estimating VaR at various confidence levels. The results indicate that both stationary and fractionally integrated GARCH models outperform RiskMetrics in estimating 1% VaR. Although most return series show fat-tailed distribution and satisfy the long memory property, it is more important to consider a model with fat-tailed error in estimating VaR. Asymmetric behavior is also discovered in the stock market data that t-error models give better 1% VaR estimates than normal-error models in long position, but not in short position. No such asymmetry is observed in the exchange rate data. |
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