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Evaluating portfolio Value-at-Risk using semi-parametric GARCH models

  • Jeroen Rombouts
  • Marno Verbeek

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.

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File URL: http://www.tandfonline.com/doi/abs/10.1080/14697680902785284
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Article provided by Taylor & Francis Journals in its journal Quantitative Finance.

Volume (Year): 9 (2009)
Issue (Month): 6 ()
Pages: 737-745

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Handle: RePEc:taf:quantf:v:9:y:2009:i:6:p:737-745
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