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采用马尔科夫链逼近的方法研究了在股票交易中的最佳清算准则的数值算法.与现有文献相比,文章具有以下特点:首先,不同于基于布朗运动的股票模型,我们采用由连续时间马尔科夫链驱动的动态模型.其次,我们着重解决大宗股票的交易问题.和我们之前文章中通过动态规划把相应的问题转化为带状态约束的HJB(Hamilton-Jacobi-Bellman)方程的方法略有不同,我们采取马尔科夫链逼近的方法,对数值算法的收敛性进行了论证.此外,我们进一步提供了相应的数值实例用以演示说明.
The method of Markov chain approximation is used to study the numerical algorithm of the optimal liquidation rule in stock trading.Compared with the existing literature, the article has the following characteristics: Firstly, unlike the stock model based on Brownian motion, Time Markov chain driven dynamic model.Secondly, we focus on the trading of bulk stocks.And our previous article by dynamic programming to transform the corresponding problem into a state-constrained HJB (Hamilton-Jacobi-Bellman) equation method Slightly different, we take the approach of Markov chain approximation to demonstrate the convergence of the numerical algorithm.In addition, we further provide the corresponding numerical examples to demonstrate.