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Data cloning estimation of GARCH and COGARCH models

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Marín, J.M. and Rodríguez Bernal, Teresa (2015) Data cloning estimation of GARCH and COGARCH models. Journal of Statistical Computation and Simulation, 85 (9). pp. 1818-1831. ISSN 0094-9655

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Official URL: http://www.tandfonline.com/doi/abs/10.1080/00949655.2014.903948#.VbdViPntlBc




Abstract

GARCH models include most of the stylized facts of financial time series and they have been largely used to analyze discrete financial time series. In the last years, continuous time models based on discrete GARCH models have been also proposed to deal with non-equally spaced observations, as COGARCH model based on Lévy processes. In this paper, we propose to use the data cloning methodology in order to obtain estimators of GARCH and COGARCH model parameters. Data cloning methodology uses a Bayesian approach to obtain approximate maximum likelihood estimators avoiding numerically maximization of the pseudo-likelihood function. After a simulation study for both GARCH and COGARCH models using data cloning, we apply this technique to model the behavior of some NASDAQ time series.


Item Type:Article
Uncontrolled Keywords:GARCH , Continuous-time GARCH process , Lévy process , COGARCH , Data cloning , Bayesian inference , MCMC algorithm
Subjects:Sciences > Mathematics > Mathematical statistics
ID Code:32663
Deposited On:28 Jul 2015 10:27
Last Modified:28 Jul 2015 10:27

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