全球智库信息集成服务系统(G2TT): Cross-sectional Skewness

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w25113
来源ID	Working Paper 25113
	Cross-sectional Skewness
	Simon Oh; Jessica A. Wachter
发表日期	2018-10-01
出版年	2018
语种	英语
摘要	This paper evaluates skewness in the cross-section of stock returns in light of predictions from a well-known class of models. Cross-sectional skewness in monthly returns far exceeds what the standard lognormal model of returns would predict. However, skewness in long-run returns substantially understates what the lognormal model would predict. Nonstationary share dynamics imply a breakdown in the distinction between market and idiosyncratic risk in the lognormal model. We present an alternative model that matches the skewness in the data and implies stationary wealth shares. In this model, idiosyncratic risk is the primary driver of growth in the economy.
主题	Financial Economics ; Portfolio Selection and Asset Pricing
URL	https://www.nber.org/papers/w25113
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/582786
推荐引用方式 GB/T 7714	Simon Oh,Jessica A. Wachter. Cross-sectional Skewness. 2018.