Evaluation of VaR models' forecasting performance: the case of oil markets
AbstractThis 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.
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Bibliographic InfoArticle provided by Inderscience Enterprises Ltd in its journal Int. J. of Financial Services Management.
Volume (Year): 5 (2012)
Issue (Month): 3 ()
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Web page: http://www.inderscience.com/browse/index.php?journalID=76
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.;
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