Empirical analysis of GARCH models in value at risk estimation

Mike K. P. SO, Leung Ho Philip YU

Research output: Contribution to journalArticlespeer-review

71 Citations (Scopus)

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. Copyright © 2005 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)180-197
JournalJournal of International Financial Markets, Institutions and Money
Volume16
Issue number2
Early online dateAug 2005
DOIs
Publication statusPublished - Apr 2006

Citation

So, M. K. P., & Yu, P. L. H. (2006). Empirical analysis of GARCH models in value at risk estimation. Journal of International Financial Markets, Institutions and Money, 16(2), 180-197. doi: 10.1016/j.intfin.2005.02.001

Keywords

  • GARCH model
  • Long memory
  • Market risk

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