英文标题：
《Against the Norm: Modeling Daily Stock Returns with the Laplace
Distribution》
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作者：
David Toth, Bruce Jones
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最新提交年份：
2019
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英文摘要：
Modeling stock returns is not a new task for mathematicians, investors, and portfolio managers, but it remains a difficult objective due to the ebb and flow of stock markets. One common solution is to approximate the distribution of stock returns with a normal distribution. However, normal distributions place infinitesimal probabilities on extreme outliers, but these outliers are of particular importance in the practice of investing. In this paper, we investigate the normality of the distribution of daily returns of major stock market indices. We find that the normal distribution is not a good model for stock returns, even over several years\' worth of data. Moreover, we propose using the Laplace distribution as a model for daily stock returns.
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中文摘要：
对数学家、投资者和投资组合经理来说，建立股票回报模型并不是一项新任务，但由于股市的起伏，它仍然是一个困难的目标。一种常见的解决方案是用正态分布近似股票收益的分布。然而，正态分布给极端异常值带来了无穷小的概率，但这些异常值在投资实践中尤为重要。本文研究了主要股票市场指数日收益率分布的正态性。我们发现，正态分布并不是一个很好的股票回报模型，即使是几年的数据。此外，我们建议使用拉普拉斯分布作为每日股票收益的模型。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Mathematical Finance 数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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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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