Collaborative Research: Understanding Large Movements in Stock Market Activity

本研究的基本动机是了解幂律分布，该分布描述了股票价格波动中大幅度波动的各种特性，其特征指数似乎对世界各地不同规模的股票和不同的股票市场具有普遍性。 这个项目开发了一个金融市场微观行为的模型来解释这些经验观察到的事实。该模式将有以下关键组成部分：（a）市场参与者的行为具有战略性，即，它们寻求最小化执行时间和价格影响，以及（B）共同基金的规模分布具有指数为1的幂律尾部。 该模型的一致性进行了测试，与一系列的回报率，交易量和交易数量的经验数据所显示的等时相互依赖。 该项目在几个方向上巩固和扩展了该模型的概念框架。该模型预测，一些机制提出，以减少极端波动是无效的。例如，托宾税和断路器不会改变回报指数的值，但修改后的托宾税税率随着交易量的增加而迅速增加，从而降低了回报分布的丰满度。该模型还提出了一种使用音量来解析收益波动中的新闻噪音的方法，该项目探讨了这种方法的有用性。波动性被认为是自相关的，初步证据表明，交易数量和回报波动性也是自相关的。他们的长期记忆指数非常接近。 研究人员以系统的方式研究波动性，交易量和交易数量的传播，特别关注极端事件和长期记忆特性。 这项研究实现了一个合理的完整的描述市场活动的方式创建和传播。

The basic motivation of this investigation is to understand the power-law distributions that describe various properties characterizing large movements in stock price fluctuations, with characteristic exponents that seem to be universal for different sizes of stocks and different stock markets around the world. This project develops a model of the microbehavior of financial markets to account for these empirically observed facts. The model will have the following key components: (a) market participants behave strategically, i.e., they seek to minimize execution time and price impact, and (b) the size distribution of mutual funds has a power law tail with exponent 1. The model is tested for consistency with a series of equal-time codependences among return, volume, and number of trades that are displayed by empirical data. This project consolidates and extends the conceptual framework of the model in several directions. The model predicts that some mechanisms proposed to reduce extreme fluctuations are ineffective. For example, a Tobin tax and circuit breakers do not change the value of the exponent of returns, but a modified Tobin tax whose tax rate increases sufficiently quickly with volume traded decreases the fatness of the return distribution. The model also suggests a way to use volume to parse out noise from news in return fluctuations, and this project explores how useful this approach is. Volatility is known to be autocorrelated, and preliminary evidence suggests that number of trades and return volatility are similarly autocorrelated. Their long-term memory exponents are very close. The investigators study the propagation of volatility, volume and number of trades in a systematic way, particularly focusing on the extreme events and the long-term memory properties. This study achieves a reasonably complete description of the way market activity is created and propagates.