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

被引：1

作者：

Wei, Chao ^{[1
]}

机构：

[1] Anyang Normal Univ, Sch Math & Stat, Anyang 455000, Peoples R China

来源：

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS | 2022年 / 15卷 / 02期

关键词：

parameter estimation; discrete observation; consistency; asymptotic normality; mean-reversion type; stochastic differential equations; Girsanov transformation; approximate likelihood function; Burkholder-Davis-Gundy inequality; Holder inequality; MAXIMUM-LIKELIHOOD-ESTIMATION; DIFFUSION; MODELS;

D O I：

10.1504/IJCSM.2022.124000

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：117 / 131

页数：15

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