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Comparing Univariate and Multivariate Models to Forecast Portfolio Value-at-Risk

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  • André A. P. Santos
  • Francisco J. Nogales
  • Esther Ruiz

Abstract

This article compares multivariate and univariate Generalized Autoregressive Conditional Heteroskedasticity (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 article, the dynamic conditional correlation model with Student's t errors seems to be the most appropriate specification when implemented to estimate the VaR of the real portfolios analyzed. Copyright The Author, 2012. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

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Bibliographic Info

Article provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.

Volume (Year): 11 (2013)
Issue (Month): 2 (March)
Pages: 400-441

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Handle: RePEc:oup:jfinec:v:11:y:2013:i:2:p:400-441

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References

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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Multivariate Versus Univariate Forecasts – Which is Best for Forecasting?
    by Clive Jones in Business Forecasting on 2013-06-10 15:57:40
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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Cited by:
  1. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2013. "Predicting Covariance Matrices with Financial Conditions Indexes," Tinbergen Institute Discussion Papers 13-113/III, Tinbergen Institute.
  2. Santos, André A.P. & Nogales, Francisco J. & Ruiz, Esther & Dijk, Dick Van, 2012. "Optimal portfolios with minimum capital requirements," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1928-1942.
  3. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg, 2014. "Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity," CREATES Research Papers 2014-05, School of Economics and Management, University of Aarhus.
  4. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.

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