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A theory of power-law distributions in financial market fluctuations

机译:金融市场波动中的幂律分布理论

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Insights into the dynamics of a complex system are often gained by focusing on large fluctuations. For the financial system, huge databases now exist that facilitate the analysis of large fluctuations and the characterization of their statistical behaviour(1,2). Power laws appear to describe histograms of relevant financial fluctuations, such as fluctuations in stock price, trading volume and the number of trades(3-10). Surprisingly, the exponents that characterize these power laws are similar for different types and sizes of markets, for different market trends and even for different countries - suggesting that a generic theoretical basis may underlie these phenomena. Here we propose a model, based on a plausible set of assumptions, which provides an explanation for these empirical power laws. Our model is based on the hypothesis that large movements in stock market activity arise from the trades of large participants. Starting from an empirical characterization of the size distribution of those large market participants ( mutual funds), we show that the power laws observed in financial data arise when the trading behaviour is performed in an optimal way. Our model additionally explains certain striking empirical regularities that describe the relationship between large fluctuations in prices, trading volume and the number of trades. [References: 28]
机译:通常,通过关注较大的波动,可以深入了解复杂系统的动力学。对于金融系统而言,现在存在着巨大的数据库,可以方便地分析大的波动并表征其统计行为(1,2)。幂律似乎描述了相关金融波动的直方图,例如股价,交易量和交易次数的波动(3-10)。出乎意料的是,对于不同类型和大小的市场,不同市场趋势甚至不同国家而言,表征这些幂定律的指数都是相似的,这表明这些现象可能是通用的理论基础。在这里,我们提出了一个基于一组合理假设的模型,该模型为这些经验幂定律提供了解释。我们的模型基于这样的假设,即股票市场活动的大幅波动是由大型参与者的交易引起的。从那些大型市场参与者(共同基金)的规模分布的经验表征开始,我们表明,当以最佳方式执行交易行为时,金融数据中观察到的幂定律就会出现。我们的模型还解释了一些惊人的经验规律,这些规律描述了价格,交易量和交易数量的大幅度波动之间的关系。 [参考:28]

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