Normal mixture quasi maximum likelihood estimation for non-stationary TGARCH(1,1) models

Hui Wang, Jiazhu Pan

Research output: Contribution to journalArticle

1 Citation (Scopus)
101 Downloads (Pure)

Abstract

Although quasi maximum likelihood estimator based on Gaussian density (G-QMLE) is widely used to estimate GARCH-type models, it does not perform successfully when error distribution is either skewed or leptokurtic. This paper proposes normal mixture quasi-maximum likelihood estimator (NM-QMLE) for non-stationary TGARCH(1,1) models. We show that, under mild regular conditions, there is no consistent estimator for the intercept, and the proposed estimator for any other parameter is consistent.
Original languageEnglish
Pages (from-to)117–123
Number of pages7
JournalStatistics and Probability Letters
Volume91
Early online date12 Apr 2014
DOIs
Publication statusPublished - 1 Aug 2014

Keywords

  • non-stationary TGARCH models
  • consistency
  • quasi-maximum likelihood estimator

Fingerprint Dive into the research topics of 'Normal mixture quasi maximum likelihood estimation for non-stationary TGARCH(1,1) models'. Together they form a unique fingerprint.

  • Profiles

    Cite this