作者:Yan, HH (Yan, Hanhuan)[ 1 ] ; Han, LY (Han, Liyan)[ 1 ]
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
卷: 514
页: 473-486
DOI: 10.1016/j.physa.2018.09.080
出版年: JAN 15 2019
文献类型:Article
摘要
A preponderance of research evidence shows that normal distributions cannot capture the behaviour of stock returns. In some empirical experiments, alternatives to normal distributions have been applied to stock data on a case-by-case basis, but no simple and practical general solutions exist to capture stock behaviour. As a simple methodology, the normal mixture model is a linear combination of normal distributions that can be directly used to approximate the characteristics of stock returns. In this paper, we recommend usage of the normal mixture model as a general method to understand stock behaviour. We also compare the performance of different normal mixture models with kernel density estimations for ten major stock market indexes and two individual stocks from the years 2000 to 2016. Empirical results show that the normal mixture model with three components better represents the behaviour of stock returns, both statistically and economically, than models based on normal distributions and kernel density estimations. (C) 2018 Elsevier B.V. All rights reserved.
关键词
作者关键词:Stock returns; Normal mixture distribution; Kernel density estimation; Kolmogorov Smirnov statistic; Stock behaviour
KeyWords Plus:MODELS; FLUCTUATIONS; PRICES; CHINA
通讯作者地址:
Beihang University Beihang Univ, Sch Econ & Management, 37 Xueyuan Rd, Beijing, Peoples R China.
通讯作者地址: Han, LY (通讯作者)
