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Econometrics 2016, 4(1), 3; doi:10.3390/econometrics4010003

Forecasting Value-at-Risk under Different Distributional Assumptions

1
Center for Operations Research and Econometrics (CORE), Université catholique de Louvain, 34 Voie du Romans Pays, B-1348 Louvain-la-Neuve, Belgium
2
Center for Research in Finance and Management (CeReFiM), Université de Namur, 62 Rue de Bruxelles, B-5000 Namur, Belgium
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Fredj Jawadi, Tony S. Wirjanto and Nuttanan Wichitaksorn
Received: 15 July 2015 / Revised: 16 December 2015 / Accepted: 21 December 2015 / Published: 11 January 2016
(This article belongs to the Special Issue Recent Developments of Financial Econometrics)
View Full-Text   |   Download PDF [1192 KB, uploaded 11 January 2016]   |  

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. View Full-Text
Keywords: Value-at-Risk; forecast accuracy; distributions; backtesting Value-at-Risk; forecast accuracy; distributions; backtesting
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Braione, M.; Scholtes, N.K. Forecasting Value-at-Risk under Different Distributional Assumptions. Econometrics 2016, 4, 3.

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