Evaluating Portfolio Value-at-Risk using Semi-Parametric GARCH Models

JVK Rombouts, Marno Verbeek

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)

Abstract

In this paper we examine the usefulness of multivariate semi-parametric GARCH models for portfolio selection under a Value-at-Risk (VaR) constraint. 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.
Original languageEnglish
Pages (from-to)737-745
Number of pages9
JournalQuantitative Finance
Volume9
Issue number6
DOIs
Publication statusPublished - 2009

Research programs

  • RSM F&A

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