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| Author: | Choudhry, T. Wu, H. |
| Title: | Forecasting the weekly time-varying beta of UK firms: GARCH models vs. Kalman filter method |
| Journal: | European Journal of Finance
2009 : APR-JUN, VOL. 15:3-4, p. 437-444 |
| Index terms: | forecasting filtering techniques models volatility beta factor United Kingdom companies |
| Freeterms: | Kalman filter GARCH time-varying |
| Language: | eng |
| Abstract: | The author examines the forecasting ability of three different Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models and the Kalman filter method. The three GARCH models applied are: bivariate GARCH, BEKK GARCH, and GARCH-GJR. Based on 20 UK company's weekly stock return (based on time-varying beta) forecasts are employed to evaluate the out-of-sample forecasting ability of both the GARCH models and the Kalman method of forecast errors. 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. |
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