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Applications of Stochastic Control Theory for Portfolio Optimization

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thesis
posted on 13.09.2021, 07:56 by WEI NING
The present work is devoted to investigating portfolio optimization. In the first part, we address the problem of robust utility maximization, where we consider the uncertainty in the drift and covariance matrix of the securities. In the second part, we aim at steering the portfolio wealth to a prescribed terminal distribution. We study this problem with the tools of optimal mass transport. We designed two deep neural network-based algorithms to solve optimal transport problems. The first deep learning algorithm is based on the penalization of the terminal constraint. In the second algorithm, we solve the dual problem with adversarial networks.

History

Principal supervisor

Gregoire Loeper

Year of Award

2021

Department, School or Centre

Mathematics

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Science

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