Evaluation of VaR models' forecasting performance: the case of oil markets

Mohamed Gallali and Raggad Zahraa

International Journal of Financial Services Management, 2012, vol. 5, issue 3, 197-215

Abstract: This paper highlights the importance of Value-at-Risk (VaR) methodology in managing oil market risks of three international crude oil rates (Brent, OPEP and WTI). Comparing between the conventional VaR models proposed by the literature (non-parametric models, hybrid models and conditional and unconditional parametric models), we point to the supremacy of conditional GARCH-type models (GARCH-T) or hybrid models (Filtered Historical Simulation). In contrast, the unconditional models or those based on the normality hypothesis are the least performing. In general, there is a tendency to prefer the conditional models as they allow integrating the dynamic nature of volatility and distributions flat tails.

Keywords: risk management; oil markets; VaR; value-at-risk; variance-covariance method; historical simulation; conditional models; backtesting; forecasting performance; crude oil rates; hybrid models; volatility; flat tails. (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=46935 (text/html)
Access to full text is restricted to subscribers.

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: https://EconPapers.repec.org/RePEc:ids:ijfsmg:v:5:y:2012:i:3:p:197-215

Access Statistics for this article

More articles in International Journal of Financial Services Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().