Data-driven Distributionally Robust Stochastic Optimization via Wasserstein Distance with Applications to Portfolio Risk Management and Inventory Control
2020-12
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Data-driven Distributionally Robust Stochastic Optimization via Wasserstein Distance with Applications to Portfolio Risk Management and Inventory Control
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2020-12
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Abstract
The central theme of this dissertation is stochastic optimization under distributional ambiguity. One canthink of this as a two player game between a decision maker, who tries to minimize some loss or maximize
some reward, and an adversarial agent that chooses the worst case, or least favorable, distribution (to the
decision maker) from some ambiguity set. The Wasserstein distance metric is used to specify the ambiguity
set which is known as a Wasserstein ball of some finite radius d. At the center of this ball, is the empirical
distribution, which serves as a proxy for the true underlying distribution. In that sense, this line of research
has been called data-driven robust optimization in the academic literature. The primal problem is infinite
dimensional since the Wasserstein ball contains all finite and discrete distributions within distance d of the
empirical distribution. As such, it would appear more difficult to solve the stochastic optimization problem
in this setting.
This research makes use of (recent) Lagrangian duality results in distributional robustness and (classical)
moments duality results to formulate and solve the simpler finite dimensional dual problem. Different
problem formulations are considered, both with and without moment constraints on the ambiguity set. Some
interesting practical applications of these results include single stage and multistage problems in portfolio
risk management and inventory control. We also investigate the notion of time consistency between the static
and dynamic (multi-period) problem formulations. Time consistency is a desirable property in that the decision
maker knows that the optimal policy determined at time zero will not change as realizations of the data
process and corresponding system state are observed.
In particular, this dissertation considers optimal decision making for portfolio problems in counterparty
credit risk, funding risk, statistical arbitrage, option exercise, asset purchasing/selling, and quantification of
certain profit and risk metrics. In addition, we consider the classical newsvendor model (both with and without
moment constraints) in the single period and multi-period settings. We conclude with some commentary
on our findings throughout this work and provide some suggestions for further research.
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University of Minnesota Ph.D. dissertation. December 2020. Major: Industrial Engineering. Advisor: Shuzhong Zhang. 1 computer file (PDF); xi, 190 pages.
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Singh, Derek. (2020). Data-driven Distributionally Robust Stochastic Optimization via Wasserstein Distance with Applications to Portfolio Risk Management and Inventory Control. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/218682.
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