Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171846
Authors: 
Braione, Manuela
Scholtes, Nicolas K.
Year of Publication: 
2016
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 4 [Year:] 2016 [Issue:] 1 [Pages:] 1-27
Abstract: 
Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR). We provide a comprehensive look at the problem by considering the impact that different distributional assumptions have on the accuracy of both univariate and multivariate GARCH models in out-of-sample VaR prediction. The set of analyzed distributions comprises the normal, Student, Multivariate Exponential Power and their corresponding skewed counterparts. The accuracy of the VaR forecasts is assessed by implementing standard statistical backtesting procedures used to rank the different specifications. The results show the importance of allowing for heavy-tails and skewness in the distributional assumption with the skew-Student outperforming the others across all tests and confidence levels.
Subjects: 
Value-at-Risk
forecast accuracy
distributions
backtesting
JEL: 
C01
C22
C52
C58
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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