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Activity Number: 411 - Copula Model and Maximum Likelihood Estimation
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #328882
Title: Asymptotic Theory of Maximum Likelihood Estimator for Jump-Diffusion Model
Author(s): Yongxin Ye*
Companies: Peking University
Keywords: jump-diffusion; maximum likelihood estimation; asymptotic theory; Radon-Nikodym derivative

This paper derives the asymptotics of the maximum likelihood estimators for jump-diffusion models. We propose the decomposition of the discrete-time likelihood function by Radon-Nikodym derivative, which plays an important role in the derivation for the asymptotics. Then we derive the corresponding score and Hessian functions. The asymptotic theory is therefore established as the sampling interval goes to zero and the time span goes to infinity. Our theory reveals that the asymptotic distributions of drift and diffusion parameters are independent of the jump parameters, and they are the same as those under the pure diffusion models in form. The accuracy of our theory is illustrated through some representative examples.

Authors who are presenting talks have a * after their name.

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