Monash University
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Distributionally Robust Optimization and its Applications in Portfolio Optimization

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thesis
posted on 2022-12-17, 09:35 authored by XIN HAI
We formulate a framework to solve the distributionally robust optimization problem. We allow the true probability measure to be inside a Wasserstein ball that is specified by the empirical data and the given confidence level. We transform the robust optimization into a non-robust optimization with a penalty term and provide an appropriate selection of the Wasserstein ambiguity set’s size. Then we apply the framework to some portfolio optimization problems, such as the mean-CVaR in risk management and the multiperiod mean-variance in portfolio management. Moreover, the numerical experiments of the US stock market showed the impressive results for our robust models compared to other popular strategies.

History

Campus location

Australia

Principal supervisor

Kihun Nam

Additional supervisor 1

Gregoire Loeper

Year of Award

2022

Department, School or Centre

Mathematics

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Science

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