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Modelling and Forecasting Multivariate Realized Volatility

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  • Roxana Chiriac
  • Valeri Voev

    ()
    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

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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File URL: ftp://ftp.econ.au.dk/creates/rp/08/rp08_39.pdf
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Bibliographic 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
Date of revision:
Handle: RePEc:aah:create:2008-39

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

Related research

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

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