英文标题：
《Evidence for criticality in financial data》
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作者：
G. Ruiz L\\\'opez and A. Fern\\\'andez de Marcos
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最新提交年份：
2017
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英文摘要：
We provide evidence that cumulative distributions of absolute normalized returns for the $100$ American companies with the highest market capitalization, uncover a critical behavior for different time scales $\\Delta t$. Such cumulative distributions, in accordance with a variety of complex --and financial-- systems, can be modeled by the cumulative distribution functions of $q$-Gaussians, the distribution function that, in the context of nonextensive statistical mechanics, maximizes a non-Boltzmannian entropy. These $q$-Gaussians are characterized by two parameters, namely $(q,\\beta)$, that are uniquely defined by $\\Delta t$. From these dependencies, we find a monotonic relationship between $q$ and $\\beta$, which can be seen as evidence of criticality. We numerically determine the various exponents which characterize this criticality.
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中文摘要：
我们提供的证据表明，市值最高的100美元美国公司的绝对标准化收益的累积分布揭示了不同时间尺度下的关键行为$\\ Delta t$。根据各种复杂的金融系统，这种累积分布可以用$q$-高斯的累积分布函数来建模，该分布函数在非扩展统计力学的背景下，使非玻尔兹曼熵最大化。这些$q$-高斯由两个参数表征，即$（q、\\beta）$，由$\\ Delta t$唯一定义。从这些依赖关系中，我们发现$q$和$\\ beta$之间存在单调关系，这可以看作是临界性的证据。我们从数值上确定了表征这种临界状态的各种指数。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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