This article proposes a microstructure model for stock prices in which parameters are modulated by a Markov chain determining the market behaviour. In this approach, called the switching microstructure model (SMM), the stock price is the result of the balance between the supply and the demand for shares. The arrivals of bid and ask orders are represented by two mutually- and self-excited processes. The intensities of these processes converge to a mean reversion level that depends upon the regime of the Markov chain. The first part of this work studies the mathematical properties of the SMM. The second part focuses on the econometric estimation of parameters. For this purpose, we combine a particle filter with a Markov chain Monte Carlo algorithm. Finally, we calibrate the SMM with two and three regimes to daily returns of the S&P 500 and compare them with a non switching model.

中文翻译：

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股票价格的转换微观结构模型

本文提出了一种股票价格的微观结构模型，其中参数由确定市场行为的马尔可夫链进行调节。在这种称为切换微结构模型（SMM）的方法中，股票价格是股票供需之间平衡的结果。出价和要价订单的到来由两个相互激励的过程组成。这些过程的强度收敛到平均回复水平，该水平取决于马尔可夫链的状态。这项工作的第一部分研究了SMM的数学特性。第二部分着重于参数的计量经济学估计。为此，我们将粒子滤波器与马尔可夫链蒙特卡罗算法结合在一起。最后，我们用两种和三种方式对SMM进行校准，以计算S＆