Construction of value-at-risk forecasts under different distributional assumptions within a BEKK framework

Manuela Braione () and Nicolas Scholtes
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Manuela Braione: Université catholique de Louvain, CORE, Belgium

No 2014059, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. In order to account for both the skewness and the excess kurtosis in returns, we combine the BEKK model from the multivariate GARCH literature with different multivariate densities for the returns. The set of distributions we consider comprises the normal, Student, Multivariate Exponential Power and their skewed counterparts. Applying this framework to a sample of ten assets from the Dow Jones Industrial Average Index, we compare the performance of equally- weighted portfolios derived from the symmetric and skewed distributions in forecasting out-of-sample Value-at-Risk. The accuracy of the VaR forecasts is assessed by implementing standard statistical backtesting procedures. The results unanimously show that the inclusion of fat-tailed densities into the model specification yields more accurate VaR forecasts, while the further addition of skewness does not lead to significant improvements.

Keywords: Dow Jones industrial average; BEKK model; maximum likelihood; value-at-risk (search for similar items in EconPapers)
JEL-codes: C01 C22 C52 C58 (search for similar items in EconPapers)
Date: 2014-11-18
New Economics Papers: this item is included in nep-ban, nep-ecm, nep-for and nep-rmg
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