Study on Tail Characteristics of Systemic Risk in US and Chinese Stock Markets

Yujie Lai,Yibo Hu

doi:10.54691/bcpbm.v17i.352

Abstract

Trade friction between the United States and China has increased financial risks. Since 2017, the stock markets of the United States and China have shown extremely high-risk dependence. By constructing Markov SJC Copula model, this paper makes an empirical analysis on the systemic risk of American and Chinese stock markets. The results show that SJC Copula can describe the systemic risk of American and Chinese stock markets well. In addition, studies show that the risk dependence of the U.S. and Chinese stock markets also has obvious tail dynamic characteristics. Under the high-risk zone system, the dependence of the lower tail risk is more significant.

Highlights

Previous studies have mainly examined linear and symmetric systemic risks under normal market conditions
The Copula method is an important method in the study of the correlation structure of financial market variables
In order to obtain the possible tail dynamic characteristics of risk dependence in the U.S and China's stock markets, SJC copula with the best fitting effect is selected, and the corresponding Markov mechanism transformation copula model is constructed for analysis

Summary

Introduction

Previous studies have mainly examined linear and symmetric systemic risks under normal market conditions. It can flexibly and effectively capture the complex correlation structure between financial market variables Such as nonlinear correlation, tail correlation under extreme market conditions, asymmetric correlation and dynamic correlation, etc. Copula allows modeling the marginal distribution and correlation structure of financial market variables respectively, and constructs complex non-normal joint distribution by selecting different marginal distribution and Copula. This increases the flexibility of modeling and helps to fully characterize the non-normal characteristics of financial market variables such as "skew", "leptokurtosis and fat-tail". By examining the tail correlation structure of stock market volatility in the United States and China under extreme market conditions, selecting the appropriate Copula function and constructing the corresponding Markov mechanism to transform the Copula model, the systemic risk in the United States and China stock market is studied, and the possible asymmetric and tail dynamic characteristics of risk dependence are investigated

Model Parameter Estimation

Data and Descriptive Statistics

Markov SJC Copula Parameter Estimation Results

Conclusion