The University of Southampton
University of Southampton Institutional Repository

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
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
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. 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.

Text
51606.pdf - Other
Download (302kB)

More information

Published date: June 2007
Venue - Dates: 27th International Symposium on Forecasting: Financial Forecasting in a Global Economy, 2007-06-24 - 2007-06-27
Keywords: forecasting, kalman filter, garch, volatility

Identifiers

Local EPrints ID: 51606
URI: https://eprints.soton.ac.uk/id/eprint/51606
PURE UUID: d9733c98-7949-4b9a-b430-27012e4948f9
ORCID for Taufiq Choudhry: ORCID iD orcid.org/0000-0002-0463-0662

Catalogue record

Date deposited: 06 Jun 2008
Last modified: 24 Jul 2019 00:36

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×