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Modelling and Forecasting Multivariate Realized Volatility

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Author Info
Roxana Chiriac
Valeri Voev () (School of Economics and Management, University of Aarhus, Denmark and CREATES)

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Abstract

This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model’s forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies that any risk-averse investor, regardless of the type of utility function, would be better-off using our model.

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Publisher Info
Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2008-39.

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Length: 33
Date of creation: 02 Sep 2008
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Handle: RePEc:aah:create:2008-39

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Web page: http://www.econ.au.dk/afn/

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Related research
Keywords: Forecasting; Fractional integration; Stochastic dominance; Portfolio optimization; Realized covariance;

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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  5. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March. [Downloadable!] (restricted)
    Other versions:
  6. Lamoureux, Christopher G & Lastrapes, William D, 1990. " Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-29, March. [Downloadable!] (restricted)
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    Other versions:
  9. Fishburn, Peter C., 1980. "Continua of stochastic dominance relations for unbounded probability distributions," Journal of Mathematical Economics, Elsevier, vol. 7(3), pages 271-285, December. [Downloadable!] (restricted)
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    Other versions:
  11. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
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    Other versions:
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  15. Mihaela Şerban & Anthony Brockwell & John Lehoczky & Sanjay Srivastava, 2007. "Modelling the Dynamic Dependence Structure in Multivariate Financial Time Series," Journal of Time Series Analysis, Blackwell Publishing, vol. 28(5), pages 763-782, 09. [Downloadable!] (restricted)
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  21. Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Whang, 2005. "Consistent Testing for Stochastic Dominance under General Sampling Schemes," Review of Economic Studies, Blackwell Publishing, vol. 72(3), pages 735-765, 07. [Downloadable!] (restricted)
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Bonato, Matteo & Caporin, Massimiliano & Ranaldo, Angelo, 2009. "Forecasting realized (co)variances with a block structure Wishart autoregressive model," Working Papers 2009-3, Swiss National Bank. [Downloadable!]
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