A maximum principle for partial information backward stochastic control problems with applications

Jianhui Huang, Guangchen Wang, Jie Xiong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

48 Citations (Scopus)

Abstract

This paper studies the partial information control problems of backward stochastic systems. There are three major contributions made in this paper: (i) First, we obtain a new stochastic maximum principle for partial information control problems. Our method relies on a direct calculation of the derivative of the cost functional. (ii) Second, we introduce two classes of partial information linear-quadratic backward control problems for the first time and then investigate them using the maximum principle. Complete and explicit solutions are obtained in terms of some forward and backward stochastic differential filtering equations. (iii) Last but not least, we study a class of full information stochastic pension fund optimization problems which can be viewed as a special case of our general partial information ones. Applying the aforementioned maximum principle, we derive the optimal contribution policy in closed-form and present some related economic remarks.
Original languageEnglish
Pages (from-to)2106-2117
Number of pages12
JournalSIAM Journal on Control and Optimization
Volume48
Issue number4
DOIs
Publication statusPublished - 5 Aug 2009

Keywords

  • Backward stochastic differential equation
  • Linear-quadratic control
  • Maximum principle
  • Partial information
  • Pension fund
  • Stochastic filtering

ASJC Scopus subject areas

  • Control and Optimization
  • Applied Mathematics

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