Year: 2008
Type: article
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.
Authors: TAEWOOK LEE, SANGYEOL LEE
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W1963766932