This paper presents a simple forecasting technique for variance covariance matrices. It relies significantly on the contribution of Chiriac and Voev (2010) who propose to forecast elements of the Cholesky decomposition which recombine to form a positive definite forecast for the variance...

In this paper we propose a novel methodology for forecasting variance convariance matrices (VCM) using kernel estimates. While the popular Riskmetrics methodology can be seen as a special case of our methodology, the generalisation is significant as it allows the researcher to use a number of...

This paper presents a simple forecasting technique for variance covariance matrices. It relies significantly on the contribution of Chiriac and Voev (2010) who propose to forecast elements of the Cholesky decomposition which recombine to form a positive definite forecast for the variance...

The forecasting of variance-covariance matrices is an important issue. In recent years an increasing body of literature has focused on multivariate models to forecast this quantity. This paper develops a nonparametric technique for generating multivariate volatility forecasts from a weighted...

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...

Forecasting volatility has received a great deal of research attention, with the relative performance of econometric models based on time-series data and option implied volatility forecasts often being considered. While many studies find that implied volatility is the preferred approach, a...