Electronic Thesis and Dissertation Repository

Degree

Doctor of Philosophy

Program

Statistics and Actuarial Sciences

Supervisor(s)

Reg Kulperger, Hao Yu

Abstract

The GARCH-in-mean process is an important extension of the standard GARCH (generalized autoregressive conditional heteroscedastic) process and it has wide applications in economics and finance. The parameter estimation of GARCH type models usually involves the quasi-maximum likelihood (QML) technique as it produces consistent and asymptotically Gaussian distributed estimators under certain regularity conditions. For a pure GARCH model, such conditions were already found with asymptotic properties of its QML estimator well understood. However, when it comes to GARCH-in-mean models those properties are still largely unknown. The focus of this work is to establish a set of conditions under which the QML estimator of GARCH-in-mean models will have the desired asymptotic properties. Some general Markov model tools are applied to derive the result.

Keywords: GARCH, GARCH-in-mean, asymptotic theory, Markov model


Share

COinS