Forecasting the time-varying beta of UK firms: GARCH models vs Kalman filter method
Forecasting the time-varying beta of UK firms: GARCH models vs Kalman filter method
This paper forecast the weekly time-varying beta of 20 UK firms by means of four
different GARCH models and the Kalman filter method. The four GARCH models
applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X
model. The paper also compares the forecasting ability of the GARCH models and the
Kalman method. Forecast errors based on return forecasts are employed to evaluate
out-of-sample forecasting ability of both GARCH models and Kalman method.
Measures of forecast errors overwhelmingly support the Kalman filter approach.
Among the GARCH models both GJR and GARCH-X models appear to provide a bit
more accurate forecasts than the bivariate GARCH model.
forecasting, kalman filter, garch, volatility
Choudhry, Taufiq
6fc3ceb8-8103-4017-b3b5-2d38efa57728
Wu, Hao
8d0e3477-dc5a-4ce8-8121-991ad1bbb48d
June 2007
Choudhry, Taufiq
6fc3ceb8-8103-4017-b3b5-2d38efa57728
Wu, Hao
8d0e3477-dc5a-4ce8-8121-991ad1bbb48d
Choudhry, Taufiq and Wu, Hao
(2007)
Forecasting the time-varying beta of UK firms: GARCH models vs Kalman filter method.
27th International Symposium on Forecasting: Financial Forecasting in a Global Economy, New York, USA.
24 - 27 Jun 2007.
51 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
This paper forecast the weekly time-varying beta of 20 UK firms by means of four
different GARCH models and the Kalman filter method. The four GARCH models
applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X
model. The paper also compares the forecasting ability of the GARCH models and the
Kalman method. Forecast errors based on return forecasts are employed to evaluate
out-of-sample forecasting ability of both GARCH models and Kalman method.
Measures of forecast errors overwhelmingly support the Kalman filter approach.
Among the GARCH models both GJR and GARCH-X models appear to provide a bit
more accurate forecasts than the bivariate GARCH model.
More information
Published date: June 2007
Venue - Dates:
27th International Symposium on Forecasting: Financial Forecasting in a Global Economy, New York, USA, 2007-06-24 - 2007-06-27
Keywords:
forecasting, kalman filter, garch, volatility
Identifiers
Local EPrints ID: 51606
URI: http://eprints.soton.ac.uk/id/eprint/51606
PURE UUID: d9733c98-7949-4b9a-b430-27012e4948f9
Catalogue record
Date deposited: 06 Jun 2008
Last modified: 16 Mar 2024 03:16
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Contributors
Author:
Hao Wu
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