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.
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