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The State Equations Methods for Stochastic Control Problems
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作者 Lijin Wang fengshan bai 《Numerical Mathematics(Theory,Methods and Applications)》 SCIE 2010年第1期79-96,共18页
The state equations of stochastic control problems,which are controlled stochastic differential equations,are proposed to be discretized by the weak midpoint rule and predictor-corrector methods for the Markov chain a... The state equations of stochastic control problems,which are controlled stochastic differential equations,are proposed to be discretized by the weak midpoint rule and predictor-corrector methods for the Markov chain approximation approach. Local consistency of the methods are proved.Numerical tests on a simplified Merton's portfolio model show better simulation to feedback control rules by these two methods, as compared with the weak Euler-Maruyama discretisation used by Krawczyk.This suggests a new approach of improving accuracy of approximating Markov chains for stochastic control problems. 展开更多
关键词 Stochastic optimal control Markov chain approximation Euler-Maruyama discretisation midpoint rule predictor-corrector methods portfolio management.
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