Forecasting the weekly time-varying beta of UK firms: GARCH models vs Kalman filter method

This paper investigates the forecasting ability of three different GARCH models and the Kalman filter method. The three GARCH models applied are the bivariate GARCH, BEKK GARCH, and GARCH-GJR. Forecast errors based on twenty UK company’s weekly stock return (based on time-vary beta) forecasts are employed to evaluate out-of-sample forecasting ability of both the GARCH models and the Kalman method. Measures of forecast errors overwhelmingly support the Kalman filter approach. Among the GARCH models, GJR appears to provide somewhat more accurate forecasts than the two other GARCH models.