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

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...

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Main Authors:	Ralf Becker, Adam Clements, Robert O'Neill
Format:	Article
Language:	English
Published:	MDPI AG 2018-02-01
Series:	Econometrics
Subjects:	volatility forecasting kernel density estimation similarity forecasting
Online Access:	http://www.mdpi.com/2225-1146/6/1/7

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spelling	doaj-6369d806b0134279a3946c82166efcaa2020-11-24T23:17:08ZengMDPI AGEconometrics2225-11462018-02-0161710.3390/econometrics6010007econometrics6010007A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market ReturnsRalf Becker0Adam Clements1Robert O'Neill2Economics, School of Social Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UKSchool of Economics and Finance, Queensland University of Technology, Brisbane City, QLD 4000, AustraliaThe Business School, University of Huddersfield, Huddersfield HD1 3DH, UKThis 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.http://www.mdpi.com/2225-1146/6/1/7volatility forecastingkernel density estimationsimilarity forecasting
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Ralf Becker Adam Clements Robert O'Neill
spellingShingle	Ralf Becker Adam Clements Robert O'Neill A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns Econometrics volatility forecasting kernel density estimation similarity forecasting
author_facet	Ralf Becker Adam Clements Robert O'Neill
author_sort	Ralf Becker
title	A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns
title_short	A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns
title_full	A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns
title_fullStr	A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns
title_full_unstemmed	A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns
title_sort	multivariate kernel approach to forecasting the variance covariance of stock market returns
publisher	MDPI AG
series	Econometrics
issn	2225-1146
publishDate	2018-02-01
description	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.
topic	volatility forecasting kernel density estimation similarity forecasting
url	http://www.mdpi.com/2225-1146/6/1/7
work_keys_str_mv	AT ralfbecker amultivariatekernelapproachtoforecastingthevariancecovarianceofstockmarketreturns AT adamclements amultivariatekernelapproachtoforecastingthevariancecovarianceofstockmarketreturns AT robertoneill amultivariatekernelapproachtoforecastingthevariancecovarianceofstockmarketreturns AT ralfbecker multivariatekernelapproachtoforecastingthevariancecovarianceofstockmarketreturns AT adamclements multivariatekernelapproachtoforecastingthevariancecovarianceofstockmarketreturns AT robertoneill multivariatekernelapproachtoforecastingthevariancecovarianceofstockmarketreturns
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A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns

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