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A Kernel Technique for Forecasting the Variance-Covariance Matrix

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  • Ralf Becker

    ()
    (University of Manchester)

  • Adam Clements

    ()
    (QUT)

  • Robert O'Neill

    (University of Manchester)

Abstract

The forecasting of variance-covariance matrices is an important issue. In recent years an increasing body of literature has focused on multivariate models to forecast this quantity. This paper develops a nonparametric technique for generating multivariate volatility forecasts from a weighted average of historical volatility and a broader set of macroeconomic variables. As opposed to traditional techniques where the weights solely decay as a function of time, this approach employs a kernel weighting scheme where historical periods exhibiting the most similar conditions to the time at which the forecast if formed attract the greatest weight. It is found that the proposed method leads to superior forecasts, with macroeconomic information playing an important role.

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File URL: http://www.ncer.edu.au/papers/documents/WPNo66.pdf
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Bibliographic Info

Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 66.

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Length: 32 pages
Date of creation: 28 Oct 2010
Date of revision:
Handle: RePEc:qut:auncer:2010_13

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Keywords: Nonparametric; variance-covariance matrix; volatility forecasting; multivariate;

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  1. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
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