Title: Parameter estimation for mean-reversion type stochastic differential equations from discrete observations

Authors: Chao Wei

Addresses: School of Mathematics and Statistics, Anyang Normal University, Anyang, 455000, China

Abstract: This paper is concerned with the parameter estimation problem for mean-reversion type stochastic differential equations from discrete observations. The Girsanov transformation is used to simplify the equation because of the expression of the drift coefficient. The approximate likelihood function is given, the consistency of the estimator and asymptotic normality of the error of estimation are proved. An example is provided to verify the results.

Keywords: parameter estimation; discrete observation; consistency; asymptotic normality; mean-reversion type; stochastic differential equations; Girsanov transformation; approximate likelihood function; Burkholder-Davis-Gundy inequality; Holder inequality.

DOI: 10.1504/IJCSM.2022.124000

International Journal of Computing Science and Mathematics, 2022 Vol.15 No.2, pp.117 - 131

Received: 21 Jun 2019
Accepted: 09 Apr 2020
Published online: 07 Jul 2022 *

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