On optimal control of mean-field stochastic systems driven by Teugels martingales via derivative with respect to measures

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Date

2020

Authors

Mokhtar Hafayed
Shahlar Meherrem

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Publisher

Taylor and Francis Ltd. michael.wagreich@univie.ac.at

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Green Open Access

Yes

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Abstract

This paper deals with partial information stochastic optimal control problem for general controlled mean-field systems driven by Teugels martingales associated with some Lévy process having moments of all orders and an independent Brownian motion. The coefficients of the system depend on the state of the solution process as well as of its probability law and the control variable. We establish a set of necessary conditions in the form of Pontryagin maximum principle for the optimal control. We also give additional conditions under which the necessary optimality conditions turn out to be sufficient. The proof of our result is based on the derivative with respect to the probability law by applying Lions derivatives and a corresponding Itô formula. As an application conditional mean-variance portfolio selection problem in incomplete market where the system is governed by some Gamma process is studied to illustrate our theoretical results. © 2020 Elsevier B.V. All rights reserved.

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Keywords

Derivative With Respect To Measures, Lévy Process, Maximum Principle, Stochastic Control, Stochastic Differential Equations Of Mean-field Type, Teugels Martingales, Brownian Movement, Maximum Principle, Optimal Control Systems, Stochastic Systems, Conditional Means, Incomplete Markets, Mean Field, Necessary Optimality Condition, Partial Information, Stochastic Control, Stochastic Optimal Control Problem, Teugels Martingale, Stochastic Control Systems, Brownian movement, Maximum principle, Optimal control systems, Stochastic systems, Conditional means, Incomplete markets, Mean field, Necessary optimality condition, Partial information, Stochastic control, Stochastic optimal control problem, Teugels martingale, Stochastic control systems

Fields of Science

0209 industrial biotechnology, 02 engineering and technology

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OpenCitations Citation Count
5

Source

International Journal of Control

Volume

93

Issue

Start Page

1053

End Page

1062
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Scopus : 5

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