Next Article in Journal
Lasso Maximum Likelihood Estimation of Parametric Models with Singular Information Matrices
Previous Article in Journal
Estimating Unobservable Inflation Expectations in the New Keynesian Phillips Curve
Previous Article in Special Issue
Time-Varying Window Length for Correlation Forecasts
Article Menu

Export Article

Open AccessArticle
Econometrics 2018, 6(1), 7; doi:10.3390/econometrics6010007

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.
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)
View Full-Text   |   Download 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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Econometrics EISSN 2225-1146 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top