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论文

股市收益率高阶矩风险的产生机制检验

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  • 1. 南京大学工程管理学院, 江苏 南京 210093;
    2. 电子科技大学经济与管理学院, 四川 成都 611731

收稿日期: 2014-10-19

  修回日期: 2016-01-09

  网络出版日期: 2016-04-29

基金资助

国家自然科学基金青年资助项目(71401071);教育部人文社会科学研究青年资助项目(14YJC790025);江苏省自然科学基金青年资助项目(BK20130589)

Testing the Generation Mechanism of Higher-Order-Moment Risk in Stock Market Returns

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  • 1. School of Management and Engineering, Nanjing University, Nanjing 210093, China;
    2. School of Management and Economics, University of Electronic Science and Techonology of China, Chengdu 611731, China

Received date: 2014-10-19

  Revised date: 2016-01-09

  Online published: 2016-04-29

摘要

通过对现有理论文献的梳理,提炼了五个较为典型的关于高阶矩风险产生机制的理论假设。然后基于时变高阶矩建模思想,将这五个假设统一于同一个计量框架,并进行综合地实证检验,以期发掘具有"占优"作用的理论解释。以沪深股市收益率为样本研究发现,在这五个假设中,仅"波动率反馈"效应和"利空信息揭示"效应获得显著的实证支持。进一步分析指出,这两种效应会同时引起偏度和峰度风险,因而是高阶矩风险的主要产生机制。这一结果可为后续研究发展一个统一的理论框架提供实证参考。

本文引用格式

方立兵, 曾勇 . 股市收益率高阶矩风险的产生机制检验[J]. 中国管理科学, 2016 , 24(4) : 27 -36 . DOI: 10.16381/j.cnki.issn1003-207x.2016.04.004

Abstract

Five pieces of theoretical prediction on the generation of higher order moment risk are refined and thus the corresponding hypotheses are formed after reviewing the existed literature. Employing the idea of modeling time variant higher-order-moment, these five pieces of hypothesis are involved in a unified econometric framework. An empirical analysis is conducted based on such model to find some dominated theoretical explanations. Sampling daily returns from Shanghai and Shenzhen stock market composite index, the results show that there are two hypotheses relevant to volatility feedback effect and bad news revelation effect are significantly supported but the other three are not. Further analysis indicates that these two effects can generate both skewness and kurtosis risk. Therefore, they are implied as the main generation mechanism of higher-order-moment risk. These results get out of the mess of opinions on the generation mechanism of higher-order-moment risk and thus can benefit further exploration of such topic under a unified theoretical framework.

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