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A Kernel Technique for Forecasting the Variance-Covariance Matrix

  • Ralf Becker
  • Adam Clements
  • Robert O'Neill

In this paper we propose a novel methodology for forecasting variance convariance matrices (VCM) using kernel estimates. While the popular Riskmetrics methodology can be seen as a special case of our methodology, the generalisation is significant as it allows the researcher to use a number of variables to determine the kernel weights of past VCM. The complexity of the methodology scales with the number of explanatory variables used and not with the size of the VCM. This, as well as the automatic positive definiteness of the VCM forecasts are major improvements on currently available forecasting methods. An empirical analysis establishes the usefulness of our proposed methodology.

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File URL: http://www.socialsciences.manchester.ac.uk/medialibrary/cgbcr/discussionpapers/dpcgbcr151.pdf
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Paper provided by Economics, The Univeristy of Manchester in its series Centre for Growth and Business Cycle Research Discussion Paper Series with number 151.

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Length: 33 pages
Date of creation: 2010
Date of revision:
Handle: RePEc:man:cgbcrp:151
Contact details of provider: Postal: Manchester M13 9PL
Phone: (0)161 275 4868
Fax: (0)161 275 4812
Web page: http://www.socialsciences.manchester.ac.uk/subjects/economics/our-research/centre-for-growth-and-business-cycle-research/
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  1. Sjaastad, Larry A. & Scacciavillani, Fabio, 1996. "The price of gold and the exchange rate," Journal of International Money and Finance, Elsevier, vol. 15(6), pages 879-897, December.
  2. Sébastien Laurent & Jeroen V.K. Rombouts & Francesco Violante, 2010. "On the Forecasting Accuracy of Multivariate GARCH Models," Cahiers de recherche 1021, CIRPEE.
  3. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
  4. Adam Clements & Mark Doolan & Stan Hurn & Ralf Becker, 2009. "Evaluating multivariate volatility forecasts," NCER Working Paper Series 41, National Centre for Econometric Research, revised 25 Nov 2009.
  5. Becker Ralf & Clements Adam E & Hurn Stan, 2011. "Semi-Parametric Forecasting of Realized Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(3), pages 1-23, May.
  6. Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
  7. 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.
  8. Adrian R. Pagan & Kirill A. Sossounov, 2003. "A simple framework for analysing bull and bear markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 23-46.
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