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Tekijä:Choudhry, T.
Wu, H.
Otsikko:Forecasting the weekly time-varying beta of UK firms: GARCH models vs. Kalman filter method
Lehti:European Journal of Finance
2009 : APR-JUN, VOL. 15:3-4, p. 437-444
Asiasana:forecasting
filtering techniques
models
volatility
beta factor
United Kingdom
companies
Vapaa asiasana:Kalman filter
GARCH
time-varying
Kieli:eng
Tiivistelmä: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.
SCIMA tietueen numero: 272470
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