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

Marno Verbeek () and Jeroen Rombouts

No 40, Computing in Economics and Finance 2005 from Society for Computational Economics

Abstract: In this paper we examine the usefulness of multivariate semi-parametric GARCH models for portfolio selection under a Value-at-Risk (VaR) constraint. First, 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. Finally, we examine the economic value of the multivariate GARCH models by determining optimal portfolios based on maximizing expected returns subject to a VaR constraint, over a period of 500 consecutive days. Again, the superiority and robustness of the semi-parametric model is confirmed

Keywords: multivariate GARCH; semi-parametric estimation; Value-at-Risk; asset allocation (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 2005-11-11
New Economics Papers: this item is included in nep-rmg
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http://repec.org/sce2005/up.18350.1104964469.pdf (application/pdf)

Related works:
Journal Article: Evaluating portfolio Value-at-Risk using semi-parametric GARCH models (2009) Downloads
Working Paper: Evaluating portfolio value-at-risk using semi-parametric GARCH models (2009) Downloads
Working Paper: Evaluating Portfolio Value-At-Risk Using Semi-Parametric GARCH Models (2009) Downloads
Working Paper: Evaluating Portfolio Value-at-Risk using Semi-Parametric GARCH Models (2004) Downloads
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