Journal
JOURNAL OF RISK AND FINANCIAL MANAGEMENT
Volume 15, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/jrfm15020090
Keywords
ARMA models; GARCH models; QMLE; Self-weighted LSE
Categories
Ask authors/readers for more resources
This paper investigates the estimation problem of the ARMA model with GARCH noises. The consistency, asymptotic normality, and efficiency of different estimators are demonstrated through theoretical and empirical studies.
This paper studies the self-weighted least squares estimator (SWLSE) of the ARMA model with GARCH noises. It is shown that the SWLSE is consistent and asymptotically normal when the GARCH noise does not have a finite fourth moment. Using the residuals from the estimated ARMA model, it is shown that the residual-based quasi-maximum likelihood estimator (QMLE) for the GARCH model is consistent and asymptotically normal, but if the innovations are asymmetric, it is not as efficient as that when the GARCH process is observed. Using the SWLSE and residual-based QMLE as the initial estimators, the local QMLE for ARMA-GARCH model is asymptotically normal via an one-step iteration. The importance of the proposed estimators is illustrated by simulated data and five real examples in financial markets.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available