On the approximate maximum likelihood estimation for diffusion processes

TL;DR

This paper analyzes the approximate maximum likelihood estimation (AMLE) for diffusion processes, establishing its consistency, convergence rate, and conditions for asymptotic equivalence to the full MLE, based on asymptotic density expansions.

Contribution

It provides theoretical results on the consistency, convergence, and asymptotic distribution of the AMLE for diffusion processes, extending previous work by A"{}t-Sahalia.

Findings

AMLE is consistent under certain conditions.

Convergence rate depends on the number of terms in the density expansion and sampling interval.

Conditions identified for AMLE to share the same asymptotic distribution as full MLE.

Abstract

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. A\"{\i}t-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 A\"{\i}t-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.