Semiparametric Maximum Lickelihood Estimation of GARCH Models
Jian Yang
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Jian Yang: The University of Western Ontario Department of Economics, https://economics.uwo.ca/
No 9816, University of Western Ontario, Departmental Research Report Series from University of Western Ontario, Department of Economics
Abstract:
We consider a semiparametric GARCH model where the functional form for the conditional density of the errors is unknown. Adaptive conditions of the parameters are examined. Semiparametric Maximum Likelihood (SML) estimators are constructed by maximizing the nonparametric pseudo log-likelihood function computed using the residuals from initial root-n consistent estimates. SML estimators are shown to be adaptive for the adaptively estimable parameters and consistent for all identifiable parameters. Monte Carlo results suggest that SML estimators outperform quasi maximum likelihood estimators and the adaptive maximum likelihood estimators in finite samples.
Keywords: GARCH models; Adaptive estimation; Semiparametric estimation (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Date: 1998
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