%0 Journal Article
%T A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns
%A Adam Clements
%A Ralf Becker
%J -
%D 2018
%R https://doi.org/10.3390/econometrics6010007
%X 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. View Full-Tex
%K volatility forecasting
%K kernel density estimation
%K similarity forecasting
%U https://www.mdpi.com/2225-1146/6/1/7