Construction of Value at-Risk forecasts under different distributional assumptions within a BEKK framework

Nicolas Scholtes, Manuela Braione

Research output: Working paperDiscussion paper

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.
Original languageEnglish
Number of pages37
Publication statusIn preparation - 2014

Publication series

NameCORE Discussion Paper Series
PublisherCenter for Operations Research and Econometrics

Fingerprint

Skewness
Value at risk
Skewed distribution
Multivariate GARCH
Financial assets
Kurtosis
Model specification
Backtesting
Asset returns
Out-of-sample forecasting
Assets
Inclusion

Keywords

  • Value-at-risk forecasting
  • Value-at-risk backtesting
  • Distributions
  • MGARCH

Cite this

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Construction of Value at-Risk forecasts under different distributional assumptions within a BEKK framework. / Scholtes, Nicolas; Braione, Manuela .

2014. (CORE Discussion Paper Series).

Research output: Working paperDiscussion paper

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