Time varying country risk: an assessment of alternative modelling techniques

Three different techniques for the estimation of a time-varying beta are investigated: a bivariate GARCH model, the Schwert and Seguin approach, and the Kalman filter method. These approaches are applied to a set of monthly Morgan Stanley country index data over the period 1970 to 1995 and their relative performances compared. In-sample forecast tests of the performance of each of these methods for generating conditional beta suggest that the GARCH-based estimates of risk generate the lowest forecast error although these are not necessarily significantly less than those generated by the other techniques considered.