Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
472376 | Computers & Mathematics with Applications | 2008 | 7 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
M. Ghahramani, A. Thavaneswaran,