### Abstract

Original language | English |
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Number of pages | 37 |

Publication status | In preparation - 2014 |

### Publication series

Name | CORE Discussion Paper Series |
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Publisher | Center for Operations Research and Econometrics |

### Fingerprint

### Keywords

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

### Cite this

*Construction of Value at-Risk forecasts under different distributional assumptions within a BEKK framework*. (CORE Discussion Paper Series).

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

Research output: Working paper › Discussion paper

TY - UNPB

T1 - Construction of Value at-Risk forecasts under different distributional assumptions within a BEKK framework

AU - Scholtes, Nicolas

AU - Braione, Manuela

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Value-at-risk forecasting

KW - Value-at-risk backtesting

KW - Distributions

KW - MGARCH

M3 - Discussion paper

T3 - CORE Discussion Paper Series

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

ER -