英文标题：
《Stochastic Evolution of Stock Market Volume-Price Distributions》
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作者：
Paulo Rocha, Frank Raischel, Jo\\~ao P. da Cruz, Pedro G. Lind
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最新提交年份：
2014
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英文摘要：
  Using available data from the New York stock market (NYSM) we test four different bi-parametric models to fit the correspondent volume-price distributions at each $10$-minute lag: the Gamma distribution, the inverse Gamma distribution, the Weibull distribution and the log-normal distribution. The volume-price data, which measures market capitalization, appears to follow a specific statistical pattern, other than the evolution of prices measured in similar studies. We find that the inverse Gamma model gives a superior fit to the volume-price evolution than the other models. We then focus on the inverse Gamma distribution as a model for the NYSM data and analyze the evolution of the pair of distribution parameters as a stochastic process. Assuming that the evolution of these parameters is governed by coupled Langevin equations, we derive the corresponding drift and diffusion coefficients, which then provide insight for understanding the mechanisms underlying the evolution of the stock market.
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中文摘要：
利用纽约证券市场（NYSM）的可用数据，我们测试了四种不同的双参数模型，以拟合每10美元-分钟滞后的相应量价分布：伽马分布、逆伽马分布、威布尔分布和对数正态分布。衡量市值的量价数据似乎遵循特定的统计模式，而不是类似研究中衡量的价格演变。我们发现，与其他模型相比，逆伽马模型对量价演变的拟合更好。然后，我们将逆伽马分布作为NYSM数据的模型，并将分布参数对的演化作为一个随机过程进行分析。假设这些参数的演化由耦合的朗之万方程控制，我们推导出相应的漂移和扩散系数，从而为理解股票市场演化的机制提供见解。
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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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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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