Science in China Series F-Information Sciences 2009, 52(6) 983-998 DOI:   10.1007/s11432-009-0099-4  ISSN: 1009-2757 CN: 11-4426/N

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Keywords
impulse stochastic control - geometric Brownian motion - variational equation - optimal control
Authors
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The generalization of a class of impulse stochastic control models of a geometric Brownian motion

LIU XiaoPeng, LIU KunHui,

College of Science, Beijing Jiaotong University, Beijing, 100044, China

Abstract

Recently, international academic circles advanced a class of new stochastic control models of a geometric Brownian motion which is an important kind of impulse control models whose cost structure is different from the others before, and it has a broad applying background and important theoretical significance in financial control and management of investment. This paper generalizes substantially the above stochastic control models under quite extensive conditions and describes the models more exactly under more normal theoretical system of stochastic process. By establishing a set of proper variational equations and proving the existence of its solution, and applying the means of stochastic analysis, this paper proves that the generalized stochastic control models have optimal controls. Meanwhile, we also analyze the structure of optimal controls carefully. Besides, we study the solution function of variational equations in a relatively deep-going way, which constitutes the value function of control models to some extent. Because the analysis methods of this paper are greatly different from those of original reference, this paper possesses considerable originality to some extent. In addition, this paper gives the strict proof to the part of original reference which is not fairly well-knit in analyses, and makes analyses and discussions of the model have the exactitude of mathematical sense.

Keywords impulse stochastic control - geometric Brownian motion - variational equation - optimal control  
Received 2007-08-13 Revised 2008-06-05 Online:  
DOI: 10.1007/s11432-009-0099-4
Fund:

Supported by the National Natural Science Foundation of China (Grant No. 19671004)

Corresponding Authors: LIU KunHui
Email: lkhww@263.net
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