Abstract

What distribution best characterizes the time series and cross-section of individual stock returns? To answer this question, we estimate the degree of cross-sectional return skewness relative to a benchmark that nests many models considered in the literature. We find that cross-sectional skewness in monthly returns far exceeds what this benchmark model predicts. However, cross-sectional skewness in long-run returns in the data is substantially below what the model predicts. We show that fat-tailed idiosyncratic events appear to be necessary to explain skewness in the data. (JEL, G10, G11, G12, G13, G14).

中文翻译：

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横截面偏度

摘要

哪种分布最能表征个股收益的时间序列和横截面？为了回答这个问题，我们估计了横截面回报偏度相对于基准的程度，该基准嵌套了文献中考虑的许多模型。我们发现，月回报的横截面偏度远远超过了这个基准模型的预测。然而，数据中长期回报的横截面偏度远低于模型的预测。我们表明，肥尾异质事件似乎是解释数据偏度所必需的。（JEL，G10，G11，G12，G13，G14）。