摘要
本研究构建了转换杠杆效应模型,并用以研究股票市场波动率的基于已知信息的修正杠杆效应。本文将已预期信息引入非对称GARCH模型,建立了转换杠杆效应GARCH模型(Switching Leverage Effect GARCH,SLE-GARCH)来检验样本市场的修正杠杆效应。实证结果显示,九个样本市场都呈现显著的基于已知信息的修正杠杆效应和传统杠杆效应,而且市场基于已知信息对未预期消息的修正都呈现为反向修正。因此,已预期的好消息助长杠杆效应,已预期的坏消息则抵减杠杆效应,当已预期坏消息的规模超过转换杠杆效应的阀值时,修正杠杆效应将发生反转。
This study establishes a new model called SLE-GARCH and investigates the revised leverage effect by utilizing this model. This study considers that expected news could also induce asymmetric volatility so as to unexpected news. Therefore, revised leverage effect based on expected news （VLE） is taken into account and a new type of model called switching leverage effect GARCH model is developed. Nine stock market indices such as 000002. SH （Shanghai China）, S＆P500 （New York）, STI （Singapore）, FTSE （London）, AORD （Australia）, GSPTSE （Toronto）, IXIC （NASDAQ）, HSI （Hong Kong）and N225 （Tokyo） are employed in this study. Empirical results show that there is significant VLE in all markets and unexpected news will be adjusted reversely by expected news, which indicates that overreaction to expected news is presented in the sample markets. Therefore, good expected news will drive the traditional leverage effect while bad news will alleviates the leverage effect. Bad unexpected news adjusted by expected news will cause greater volatility than good ones when the expected news is bad enough to cause the reversal effect of VLE.
出处
《经济研究》
CSSCI
北大核心
2007年第11期112-122,共11页
Economic Research Journal
基金
国家自然科学基金项目(70473106
70673116)
教育部人文社会科学重点研究基地(复旦大学世界经济研究所)重大项目(05JJD790075)
国家社科基金课题(07BJY167)
中山大学"985工程"产业与区域发展研究创新基地经费资助
广东省普通高校人文社会科学重点研究基地经费资助成果之一
