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Closed form path integral based approximate solutions of stochastic differential equations

thesis
posted on 08.06.2020 by ANTONIOS MEIMARIS
Models incorporating randomness, by allowing for disturbances or random variations over time, are commonly used in a wide range of scientific disciplines. However, the existing numerical methodologies in many cases can be computationally prohibitive. Thus, in this thesis a novel computationally efficient method is developed for approximating the solutions of various models, under a range of conditions, in a closed-form manner.

History

Campus location

Australia

Principal supervisor

Athanasios Pantelous

Additional supervisor 1

Dan Zhu

Additional supervisor 2

Ioannis Kougioumtzoglou

Year of Award

2020

Department, School or Centre

Econometrics and Business Statistics

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Exports