Data-Driven Nonparametric Robust Control under Dependence Uncertainty

Erhan Bayraktar and Tao Chen

Papers from arXiv.org

Abstract: We consider a multi-period stochastic control problem where the multivariate driving stochastic factor of the system has known marginal distributions but uncertain dependence structure. To solve the problem, we propose to implement the nonparametric adaptive robust control framework. We aim to find the optimal control against the worst-case copulae in a sequence of shrinking uncertainty sets which are generated from continuously observing the data. Then, we use a stochastic gradient descent ascent algorithm to numerically handle the corresponding high dimensional dynamic inf-sup optimization problem. We present the numerical results in the context of utility maximization and show that the controller benefits from knowing more information about the uncertain model.

Date: 2022-09
New Economics Papers: this item is included in nep-upt
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Chapter: Data-Driven Non-Parametric Robust Control under Dependence Uncertainty (2023)
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