A note on GARCH model identification

3Citations
Citations of this article
12Readers
Mendeley users who have this article in their library.

Abstract

Financial returns are often modeled as autoregressive time series with innovations having conditional heteroscedastic variances, especially with GARCH processes. The conditional distribution in GARCH models is assumed to follow a parametric distribution. Typically, this error distribution is selected without justification. In this paper, we have applied the results of Thavaneswaran and Ghahramani [A. Thavaneswaran, M. Ghahramani, Applications of combining estimating functions, in: Proceedings of the International Sri Lankan Conference: Visions of Futuristic Methodologies, University of Peradeniya and Royal Melbourne Institute of Technology (RMIT), 2004, pp. 515-532] on identification of GARCH models to a number of financial data sets. © 2007 Elsevier Ltd. All rights reserved.

Cite

CITATION STYLE

APA

Ghahramani, M., & Thavaneswaran, A. (2008). A note on GARCH model identification. Computers and Mathematics with Applications, 55(11), 2469–2475. https://doi.org/10.1016/j.camwa.2007.10.001

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free