In this paper we examine the usefulness of multivariate semi-parametric GARCH models for evaluating the Value-at-Risk (VaR) of a portfolio with arbitrary weights. We specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within-sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly well, while parametric models in several cases have unacceptable failure rates. Interestingly, distributional assumptions appear to have a much larger impact on the performance of the VaR estimates than the particular parametric specification chosen for the GARCH equations.

Additional Metadata
Keywords GARCH models, Multivariate volatility, Risk management, Time series analysis
Persistent URL dx.doi.org/10.1080/14697680902785284, hdl.handle.net/1765/76384
Journal Quantitative Finance
Citation
Rombouts, J.V.K, & Verbeek, M.J.C.M. (2009). Evaluating portfolio Value-at-Risk using semi-parametric GARCH models. Quantitative Finance, 9(6), 737–745. doi:10.1080/14697680902785284