Time-varying Beta Risk of Pan-European Sectors: A Comparison of Alternative Modeling Techniques

This paper investigates the time-varying behavior of systematic risk for eighteen pan-European industry portfolios. Using weekly data over the period 1987-2005, three different modeling techniques in addition to the standard constant coefficient model are employed: a bivariate t- GARCH(1,1) model, two Kalman filter based approaches as well as a bivariate stochastic volatility model estimated via the efficient Monte Carlo likelihood technique. A comparison of the different models' ex- ante forecast performances indicates that the random-walk process in connection with the Kalman filter is the preferred model to describe and forecast the time-varying behavior of sector betas in a European context.