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Multistep predictions for multivariate GARCH models: Closed form solution and the value for portfolio management

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  • Hlouskova, Jaroslava
  • Schmidheiny, Kurt
  • Wagner, Martin

Abstract

This paper derives the closed form solution for multistep predictions of the conditional means and covariances for multivariate ARMA-GARCH models. These predictions are useful e.g. in mean-variance portfolio analysis when the rebalancing frequency is lower than the data frequency. In this situation the conditional mean and the conditional covariance matrix of the cumulated higher frequency returns are required as inputs in the mean-variance portfolio problem. The empirical value of the result is evaluated by comparing the performance of quarterly and monthly rebalanced portfolios using monthly MSCI index data across a large set of GARCH models. Using correct multistep predictions generally results in lower risk and higher returns.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 16 (2009)
Issue (Month): 2 (March)
Pages: 330-336

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Handle: RePEc:eee:empfin:v:16:y:2009:i:2:p:330-336

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Web page: http://www.elsevier.com/locate/jempfin

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Keywords: Multivariate GARCH models Volatility forecasts Portfolio optimization Minimum variance portfolio;

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Cited by:
  1. Lahiri, Kajal & Liu, Fushang, 2005. "ARCH models for multi-period forecast uncertainty-a reality check using a panel of density forecasts," MPRA Paper 21693, University Library of Munich, Germany.
  2. Francisco Rubio & Xavier Mestre & Daniel P. Palomar, 2011. "Performance analysis and optimal selection of large mean-variance portfolios under estimation risk," Papers 1110.3460, arXiv.org.
  3. Haas, Markus, 2010. "Covariance forecasts and long-run correlations in a Markov-switching model for dynamic correlations," Finance Research Letters, Elsevier, vol. 7(2), pages 86-97, June.

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