Leverage effects of financial markets in financial crisis

Author:

Li Jin1,Shao Zhi-Gang1

Affiliation:

1. Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, Guangdong Engineering Technology Research, Center of Efficient Green Energy and Environment Protection Materials, SPTE, South China Normal University, Guangzhou 510006, P. R. China

Abstract

We have investigated the leverage effects of three major financial markets within a time frame from 2000 to 2012 throughout the 2008 financial crisis. First, dividing the considered time into four consecutive periods, we find the leverage effects of markets exhibiting similar pattern at various periods. Second, splitting the yield data into the positive-return and negative-return series, we find these two series always show anti-leverage effect. The anti-leverage effect of negative-return series usually dominates over the positive one, reflecting people at most times are more sensitive to bad news. However, we observe anomalous behavior in approaching the outbreak of crisis, where the positive-return series shows stronger anti-leverage effect, i.e. people become more sensitive to good news instead. Such phenomenology can persist till after the crisis for an immature market, as opposed to a mature market where it disappears before the end of crisis without external intervene. Our results afford insight into the micro-emotion of various financial markets swept through by the financial crisis.

Funder

the National Natural Science Foundation of China

the Natural Science Foundation of Guangdong Province

Publisher

World Scientific Pub Co Pte Lt

Subject

Computational Theory and Mathematics,Computer Science Applications,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Scaling analysis of price by multi-scale Shannon entropy;International Journal of Modern Physics C;2022-09-29

2. Research on a Financial Investment Crisis Early Warning Model of Listed Companies Based on a Random Forest Algorithm;2021 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI);2021-12-24

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