Collaborative Proposal: Understanding Large Movements in Stock Activity

这项调查的基本动机是了解描述股价大幅波动的各种特征的幂规律分布，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都是通用的。这个项目开发了一个金融市场微观行为的模型，以解释这些经验性观察到的事实。该模型将有以下关键组成部分：(A)市场参与者的行为是策略性的，即他们寻求最大限度地减少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的幂函数尾部。通过经验数据显示的回报、交易量和交易数量之间的一系列等时间相关性对模型的一致性进行了检验。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，为减少极端波动而提出的一些机制是无效的。例如，托宾税和熔断机制不会改变回报指数的值，但修改后的托宾税的税率随着交易量的增加而足够快地增加，降低了回报分布的肥满度。该模型还提出了一种使用音量来解析新闻回报波动中的噪音的方法，这个项目探索了这种方法有多有用。众所周知，波动率是自相关的，初步证据表明，交易数量和回报波动率也是同样自相关的。它们的长期记忆指数非常接近。研究人员系统地研究了波动性、交易量和交易数量的传播，特别是对极端事件和长期记忆特性的研究。这项研究相当完整地描述了市场活动的创造和传播方式。

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.