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Medientyp:
E-Artikel
Titel:
Normal Mixture Quasi‐maximum Likelihood Estimator for GARCH Models
Beteiligte:
LEE, TAEWOOK;
LEE, SANGYEOL
Erschienen:
Wiley, 2009
Erschienen in:
Scandinavian Journal of Statistics, 36 (2009) 1, Seite 157-170
Sprache:
Englisch
DOI:
10.1111/j.1467-9469.2008.00624.x
ISSN:
0303-6898;
1467-9469
Entstehung:
Anmerkungen:
Beschreibung:
Abstract. The generalized autoregressive conditional heteroscedastic (GARCH) model has been popular in the analysis of financial time series data with high volatility. Conventionally, the parameter estimation in GARCH models has been performed based on the Gaussian quasi‐maximum likelihood. However, when the innovation terms have either heavy‐tailed or skewed distributions, the quasi‐maximum likelihood estimator (QMLE) does not function well. In order to remedy this defect, we propose the normal mixture QMLE (NM‐QMLE), which is obtained from the normal mixture quasi‐likelihood, and demonstrate that the NM‐QMLE is consistent and asymptotically normal. Finally, we present simulation results and a real data analysis in order to illustrate our findings.