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

Ralf Becker, Adam Clements and Robert O'Neill
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Ralf Becker: Economics, School of Social Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK
Robert O'Neill: The Business School, University of Huddersfield, Huddersfield HD1 3DH, UK

Econometrics, 2018, vol. 6, issue 1, 1-27

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.

Keywords: volatility forecasting; kernel density estimation; similarity forecasting (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2018
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