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	ON THE APPROXIMATE MAXIMUM LIKELIHOOD ESTIMATION FOR DIFFUSION PROCESSES
	Chang, Jinyuan ; Chen, Song Xi
	2011
关键词	Asymptotic expansion asymptotic normality consistency discrete time observation maximum likelihood estimation CLOSED-FORM APPROXIMATION DISCRETE OBSERVATIONS TERM STRUCTURE HIGH-FREQUENCY MODELS
英文摘要	The transition density of a diffusion process does not admit an explicit expression in general, which prevents the full maximum likelihood estimation (MLE) based on discretely observed sample paths. Ait-Sahalia [J. Finance 54 (1999) 1361-1395; Econometrica 70 (2002) 223-262] proposed asymptotic expansions to the transition densities of diffusion processes, which lead to an approximate maximum likelihood estimation (AMLE) for parameters. Built on Ait-Sahalia's [Econometrica 70 (2002) 223-262; Ann. Statist. 36 (2008) 906-937] proposal and analysis on the AMLE, we establish the consistency and convergence rate of the AMLE, which reveal the roles played by the number of terms used in the asymptotic density expansions and the sampling interval between successive observations. We find conditions under which the AMLE has the same asymptotic distribution as that of the full MLE. A first order approximation to the Fisher information matrix is proposed.; Statistics & Probability; SCI(E); SSCI; 0; ARTICLE; 6; 2820-2851; 39
语种	英语
出处	SCI
出版者	统计学纪事
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/321775]
专题	数学科学学院
推荐引用方式 GB/T 7714	Chang, Jinyuan,Chen, Song Xi. ON THE APPROXIMATE MAXIMUM LIKELIHOOD ESTIMATION FOR DIFFUSION PROCESSES. 2011-01-01.

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