Collaborative Research: Understanding Large Movements in Stock Market Activity

这项调查的基本动机是了解幂律分布，幂律分布描述了股票价格波动的各种特性，这些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都是通用的。该项目开发了一个金融市场微观行为模型，以解释这些经验观察到的事实。该模型将具有以下关键组成部分：(a)市场参与者的行为具有战略性，即他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。该模型的一致性测试与一系列等时间的相互依存关系之间的回报，交易量和交易的数量显示了经验数据。这个项目在几个方向上巩固和扩展了模型的概念框架。该模型预测，一些旨在减少极端波动的机制是无效的。例如，托宾税和熔断机制不会改变收益指数的值，但修改后的托宾税，其税率随着交易量的增加而迅速增加，会降低收益分布的丰富性。该模型还提出了一种方法，使用音量从回报波动的新闻中解析噪音，本项目探讨了这种方法的实用性。众所周知，波动率是自相关的，初步证据表明，交易次数和回报波动率同样是自相关的。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。本研究对市场活动产生和传播的方式进行了较为完整的描述。

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.