A note on GARCH model identification

Authors:
M. Ghahramani;A. Thavaneswaran
Affiliations:
Department of Mathematics & Statistics, University of Winnipeg, Winnipeg, Manitoba, Canada;Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
Venue:
Computers & Mathematics with Applications
Year:
2008

Citing 2
Cited 1

Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
Random coefficient GARCH models

Mathematical and Computer Modelling: An International Journal

Weighted possibilistic moments of fuzzy numbers with applications to GARCH modeling and option pricing

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.09

Visualization

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.