Estimation and Properties of a Time-Varying GQARCH(1,1)-M Model | Zendy

Sofia Anyfantaki | Zendy; Antonis Demos | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Estimation and Properties of a Time-Varying GQARCH(1,1)-M Model

Author(s) -

Sofia Anyfantaki,

Antonis Demos

Publication year - 2011

Publication title -

journal of probability and statistics

Language(s) - English

Resource type - Journals

eISSN - 1687-9538

pISSN - 1687-952X

DOI - 10.1155/2011/718647

Subject(s) - estimator , markov chain monte carlo , conditional variance , bayesian probability , autoregressive conditional heteroskedasticity , markov chain , mathematics , econometrics , variance (accounting) , monte carlo method , computer science , mathematical optimization , algorithm , statistics , volatility (finance) , economics , accounting

Time-varying GARCH-M models are commonly used in econometrics and financial economics. Yet the recursive nature of the conditional variance makes exact likelihoodanalysis of these models computationally infeasible. This paper outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only () computational operations, where is the sample size. Furthermore, the theoretical dynamic properties of a time-varying GQARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research