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

This paper investigates the time-varying behavior of systematic risk for eighteen pan-European sectors. Using weekly data over the period 1987- 2005, four 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, a bivariate stochastic volatility model estimated via the efficient Monte Carlo likelihood technique as well as two Markov switching models. 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. Remarkably, the Markov switching models yield a worse out-of-sample performance than standard OLS.