Economics at your fingertips  

Forecasting Value-at-Risk under Different Distributional Assumptions

Manuela Braione and Nicolas Scholtes
Additional contact information
Manuela Braione: Center for Operations Research and Econometrics (CORE), Université catholique de Louvain, 34 Voie du Romans Pays, B-1348 Louvain-la-Neuve, Belgium

Econometrics, 2016, vol. 4, issue 1, 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.

Keywords: Value-at-Risk; forecast accuracy; distributions; backtesting (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (2) Track citations by RSS feed

Downloads: (external link) (application/pdf) (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Econometrics is currently edited by Prof. Dr. Kerry Patterson

More articles in Econometrics from MDPI, Open Access Journal
Series data maintained by XML Conversion Team ().

Page updated 2017-10-19
Handle: RePEc:gam:jecnmx:v:4:y:2016:i:1:p:3-:d:61992