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...

Techniques for evaluating and selecting multivariate volatility forecasts are not yet as well understood as their univariate counterparts. This paper considers the ability of different loss functions to discriminate between a competing set of forecasting models which are subsequently applied in...

This authoritative collection of papers covers a broad spectrum of topics in theoretical and applied economics and econometrics. The tone of the book is set by Paul Klemperer’s contribution on using and abusing economic theory, in which academics are encouraged to widen the scope of their...