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Forecasting ability of GARCH vs Kalman filter method: evidence from daily UK time-varying beta

Forecasting ability of GARCH vs Kalman filter method: evidence from daily UK time-varying beta
Forecasting ability of GARCH vs Kalman filter method: evidence from daily UK time-varying beta
This paper investigates the forecasting ability 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 non-GARCH model the Kalman method. Forecast errors based on 20 UK company daily stock return (based on estimated time-vary beta) 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 GJR model appear to provide somewhat more accurate forecasts than the other bivariate GARCH models.
forecasting, Kalman filter, garch, time-varying beta, volatility
0277-6693
670-689
Choudhry, Taufiq
6fc3ceb8-8103-4017-b3b5-2d38efa57728
Wu, Hao
8d0e3477-dc5a-4ce8-8121-991ad1bbb48d
Choudhry, Taufiq
6fc3ceb8-8103-4017-b3b5-2d38efa57728
Wu, Hao
8d0e3477-dc5a-4ce8-8121-991ad1bbb48d

Choudhry, Taufiq and Wu, Hao (2008) Forecasting ability of GARCH vs Kalman filter method: evidence from daily UK time-varying beta. Journal of Forecasting, 27 (8), 670-689. (doi:10.1002/for.1096).

Record type: Article

Abstract

This paper investigates the forecasting ability 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 non-GARCH model the Kalman method. Forecast errors based on 20 UK company daily stock return (based on estimated time-vary beta) 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 GJR model appear to provide somewhat more accurate forecasts than the other bivariate GARCH models.

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More information

e-pub ahead of print date: 24 September 2008
Published date: December 2008
Keywords: forecasting, Kalman filter, garch, time-varying beta, volatility
Organisations: Management

Identifiers

Local EPrints ID: 147383
URI: https://eprints.soton.ac.uk/id/eprint/147383
ISSN: 0277-6693
PURE UUID: 0c4b93b9-f907-4bec-9e1f-7a5b6ea102ca
ORCID for Taufiq Choudhry: ORCID iD orcid.org/0000-0002-0463-0662

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Date deposited: 26 Apr 2010 08:29
Last modified: 05 Nov 2019 01:54

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