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Closed form path integral based approximate solutions of stochastic differential equations

thesis
posted on 08.06.2020, 05:12 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