Estimating portfolio value-at-risk via dynamic conditional correlation MGARCH model – an empirical study on foreign exchange rates

Yuan-Hung Hsu Ku · Jai Jen Wang

26 citations0/0 files storedApplied Economics Letters

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Risk and Portfolio Optimization

Abstract

This study compares efficiencies of five Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models in terms of value at risk (VaR) backtesting on the number of prediction failures and the average deviation between VaR and realized return series. Unlike the previous literature which presumes constant correlation coefficients, a new model proposed by Engle (2002 Engle, R. 2002. Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20: 339–50. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar], the DCC model) is applied to highlight time-varying conditional correlations amongst positions, which is essential for portfolio risk management. From the empirical studies of exchange rates data including the US Dollar to British Pound, Japanese Yen and Euro Dollar, we find that the DCC model produces least prediction failures.

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References

1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 2002Dynamic Conditional Correlation 1995Multivariate Simultaneous Generalized ARCH 1992ARCH modeling in finance 1990Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized Arch Model 1988A Capital Asset Pricing Model with Time-Varying Covariances 2001Extreme Correlation of International Equity Markets 1995Is the correlation in international equity returns constant: 1960–1990?

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