The University of Southampton
University of Southampton Institutional Repository

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

Forecasting the weekly time-varying beta of UK firms: GARCH models vs Kalman filter method
Forecasting the weekly time-varying beta of UK firms: GARCH models vs Kalman filter method
This paper investigates the forecasting ability of three different GARCH models and the Kalman filter method. The three GARCH models applied are the bivariate GARCH, BEKK GARCH, and GARCH-GJR. Forecast errors based on twenty UK company’s weekly stock return (based on time-vary beta) forecasts are employed to evaluate 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.
forecasting, Kalman filter, GARCH, volatility
1351-847X
437-444
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 (2009) Forecasting the weekly time-varying beta of UK firms: GARCH models vs Kalman filter method. European Journal of Finance, 15 (3-4), 437-444. (doi:10.1080/13518470802604499).

Record type: Article

Abstract

This paper investigates the forecasting ability of three different GARCH models and the Kalman filter method. The three GARCH models applied are the bivariate GARCH, BEKK GARCH, and GARCH-GJR. Forecast errors based on twenty UK company’s weekly stock return (based on time-vary beta) forecasts are employed to evaluate 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.

Text
EJF-paper_v2.doc - Other
Download (139kB)

More information

Published date: June 2009
Keywords: forecasting, Kalman filter, GARCH, volatility

Identifiers

Local EPrints ID: 147385
URI: https://eprints.soton.ac.uk/id/eprint/147385
ISSN: 1351-847X
PURE UUID: 2c0405d5-4789-42d6-9784-da628ad1864f
ORCID for Taufiq Choudhry: ORCID iD orcid.org/0000-0002-0463-0662

Catalogue record

Date deposited: 26 Apr 2010 08:34
Last modified: 12 Nov 2019 01:52

Export record

Altmetrics

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.

×