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Open AccessArticle

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

1
Economics, School of Social Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK
2
School of Economics and Finance, Queensland University of Technology, Brisbane City, QLD 4000, Australia
3
The Business School, University of Huddersfield, Huddersfield HD1 3DH, UK
*
Author to whom correspondence should be addressed.
Econometrics 2018, 6(1), 7; https://doi.org/10.3390/econometrics6010007
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)
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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