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 asset allocation, multivariate GARCH, semi-parametric estimation, value-at-risk
JEL C14, Semiparametric and Nonparametric Methods (jel), C22, Time-Series Models; Dynamic Quantile Regressions (jel), C53, Forecasting and Other Model Applications (jel), G11, Portfolio Choice; Investment Decisions (jel), G3, Corporate Finance and Governance (jel), M, Business Administration and Business Economics; Marketing; Accounting (jel)
Persistent URL hdl.handle.net/1765/1833
Citation
Rombouts, J.V.K, & Verbeek, M.J.C.M. (2009). Evaluating Portfolio Value-At-Risk Using Semi-Parametric GARCH Models (No. ERS-2004-107-F&A). ERIM Report Series Research in Management. Retrieved from http://hdl.handle.net/1765/1833