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

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  • Taufiq Choudhry
  • Hao Wu
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    Abstract

    This paper investigates 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. Forecast errors 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. 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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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal The European Journal of Finance.

    Volume (Year): 15 (2009)
    Issue (Month): 4 ()
    Pages: 437-444

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    Handle: RePEc:taf:eurjfi:v:15:y:2009:i:4:p:437-444

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    Related research

    Keywords: forecasting; Kalman filter; GARCH; volatility;

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    Cited by:
    1. Kurach, Radosław & Stelmach, Jerzy, 2014. "Time-Varying Behaviour of Sector Beta Risk – The Case of Poland," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 139-159, March.

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