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对指令驱动市场知情交易的研究是近年来的热点问题。常用的EKOP模型存在一些缺陷,本文放宽了EKOP模型关于日内信息均匀释放以及交易者行为独立性的假设,用动态的马尔科夫状态转移模型对该模型进行了改进,并检验了改进后的知情交易概率模型在中国证券市场的适用性。通过模拟数据以及对中国证券市场交易数据的实证研究发现动态的马尔科夫状态转移模型克服了EKOP模型受买卖方数据影响而产生的系统偏误,估计的知情交易概率更符合事后检验。
Research on instruction-driven market-based transactions is a hot issue in recent years. The common EKOP model has some shortcomings. This paper relaxes the hypothesis that the EKOP model is about the uniform release of information in the day and the independence of traders. This model is improved with the dynamic Markov state transition model, and the improved knowledge The Applicability of Transaction Probability Model in China Stock Market. Through the simulation data and the empirical study on the transaction data of Chinese stock market, it is found that the dynamic Markov state transition model overcomes the system error caused by the buyer-seller data in the EKOP model. The estimated probability of informed trading is more in line with the posteriori test.