Comparing Univariate and Multivariate Models to Forecast Portfolio Value-at-Risk
Journal of Financial Econometrics, Forthcoming
44 Pages Posted: 17 Jul 2012 Last revised: 18 Aug 2013
Date Written: July 6, 2012
Abstract
This paper compares multivariate and univariate GARCH models to forecast portfolio value-at-risk (VaR). We provide a comprehensive look at the problem by considering realistic models and diversified portfolios containing a large number of assets, using both simulated and real data. Moreover, we rank the models by implementing statistical tests of comparative predictive ability. We conclude that multivariate models outperform their univariate counterparts on an out-of-sample basis. In particular, among the models considered in this paper, the dynamic conditional correlation model with Student-t errors seems to be the most appropriate specification when implemented to estimate the VaR of the real portfolios analyzed.
Keywords: Backtesting, Basel Accords, market risk, composite likelihood, risk management, volatility
JEL Classification: C22, C53, G17
Suggested Citation: Suggested Citation
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