Comparing univariate and multivariate models to forecast portfolio value-at-risk

Andre Santos (andreportela@gmail.com) and Francisco J. Nogales
Authors registered in the RePEc Author Service: Esther Ruiz (ortega@est-econ.uc3m.es)

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: This article addresses the problem of forecasting portfolio value-at-risk (VaR) with multivariate GARCH models vis-à-vis univariate models. Existing literature has tried to answer this question by analyzing only small portfolios and using a testing framework not appropriate for ranking VaR models. In this work we provide a more comprehensive look at the problem of portfolio VaR forecasting by using more appropriate statistical tests of comparative predictive ability. Moreover, we compare univariate vs. multivariate VaR models in the context of diversified portfolios containing a large number of assets and also provide evidence based on Monte Carlo experiments. We conclude that, if the sample size is moderately large, multivariate models outperform univariate counterparts on an out-of-sample basis.

Keywords: Market; risk; Backtesting; Conditional; predictive; ability; GARCH; Volatility; Capital; requirements; Basel; II (search for similar items in EconPapers)
Date: 2009-11
New Economics Papers: this item is included in nep-ban, nep-ecm, nep-for, nep-reg and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Comparing Univariate and Multivariate Models to Forecast Portfolio Value-at-Risk (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:ws097222

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