Thesis Type: Postgraduate
Institution Of The Thesis: Middle East Technical University, Graduate School of Natural and Applied Sciences, Turkey
Approval Date: 2019
Thesis Language: English
Student: SEMİH ERGİŞİ
Supervisor: Ceren Vardar AcarAbstract:
Dynamic systems appear in many fields from economics to physics, from biology toengineering include randomness. Therefore, stochastic differential equations are oneof the necessary mathematical tools to model dynamic systems in these disciplines.In this study, we propose two parameter estimation methods when modelling withSDEs which are driven by Brownian motion. Maximum likelihood estimation andgeneralized method of moment techniques are used to estimate parameters and it isobtained that when the assumptions for Brownian motion satisfy, both techniques givethe same result.