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A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns

Author

Listed:
  • Ralf Becker

    (Economics, School of Social Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK)

  • Adam Clements

    (School of Economics and Finance, Queensland University of Technology, Brisbane City, QLD 4000, Australia)

  • Robert O'Neill

    (The Business School, University of Huddersfield, Huddersfield HD1 3DH, UK)

Abstract

This paper introduces a multivariate kernel based forecasting tool for the prediction of variance-covariance matrices of stock returns. The method introduced allows for the incorporation of macroeconomic variables into the forecasting process of the matrix without resorting to a decomposition of the matrix. The model makes use of similarity forecasting techniques and it is demonstrated that several popular techniques can be thought as a subset of this approach. A forecasting experiment demonstrates the potential for the technique to improve the statistical accuracy of forecasts of variance-covariance matrices.

Suggested Citation

  • Ralf Becker & Adam Clements & Robert O'Neill, 2018. "A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns," Econometrics, MDPI, vol. 6(1), pages 1-27, February.
    • Handle: RePEc:gam:jecnmx:v:6:y:2018:i:1:p:7-:d:132320
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