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

Economics, School of Social Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK
School of Economics and Finance, Queensland University of Technology, Brisbane City, QLD 4000, Australia
The Business School, University of Huddersfield, Huddersfield HD1 3DH, UK
Author to whom correspondence should be addressed.
Econometrics 2018, 6(1), 7;
Received: 29 September 2017 / Revised: 30 January 2018 / Accepted: 13 February 2018 / Published: 17 February 2018
(This article belongs to the Special Issue Volatility Modeling)
PDF [581 KB, uploaded 22 February 2018]


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. View Full-Text
Keywords: volatility forecasting; kernel density estimation; similarity forecasting volatility forecasting; kernel density estimation; similarity forecasting

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Becker, R.; Clements, A.; O'Neill, R. A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns. Econometrics 2018, 6, 7.

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