Empirical analysis of GARCH models in value at risk estimation

By Mike K. P. So, Philip S. Yu. Journal of international financial markets, institutions, and money, 2006

186 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/76cece11d73692f4259cd7b5bfe027a6f7e8a146

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Citations in the corpus

Developing a stress testing framework based on market risk models (2008, methodology): “Economic Letters 76, 59–64 and So and Yu, 2006. Empirical analysis of GARCH models in Value-at-Risk estimation. International Financial Markets, Institutions & Money 16, 180–197). Second, we tested the data for asymmetry in conditional volatility and found no consistent evidence to support its prese”
Frontiers in VaR forecasting and backtesting (2016, background): “However, given that the VaR forecasts required by the Basel accords are short run, the inclusion of long-memory is expected not to make any fundamental difference; see, for example, So and Yu (2006) supporting this result.”
A comprehensive review of Value at Risk methodologies (2014, methodology): “Examples of papers that report comparisons of these models are those by So and Yu (2006) and Beltratti and Morana (1999).”