A multivariate Kernel approach to forecasting the variance covariance of stock market returns - Ralf Becker; Adam Clements; Robert O'Neill (Econometrics, 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.
Keywords: C53; C58; 330; volatility forecasting; kernel density estimation; similarity forecasting