Financial Risk Contagion in Stock Markets: Causality and Measurement Aspects

Abstract

As global financial markets become highly dependent on each other, risk contagion among stock markets is a primary feature of progressing globalization, which poses uncertainties for government agencies. The deficiency of previous studies is that it is difficult to accurately grasp the direction of risk diffusion in different time periods, and to depict the intensity of risk contagion constantly. Research on causality and measurement of financial risk contagion based on nonlinear causality tests and dynamic Copula methods will help governments to allocate financial resources reasonably and effectively, thus promoting the sustainable development of the social economy and financial markets. Taking the Chinese stock market as an example, this paper evaluated the risk contagion effect between the Chinese stock market and six other stock markets including developed and emerging markets from January 2006 to December 2018. From the aspect of causality, the nonlinear Granger causality test was applied to the entire time period and the phased time periods involving specific events like the subprime mortgage crisis and the Chinese stock market crash. From the aspect of measurement, the dynamic Markov state transition Copula model was used to describe the asymmetrically dependent structure of markets, from which was derived the time-varying lower tail dependence coefficients. The results have been summarized as follows. Firstly, after the outbreak of the subprime mortgage crisis, the stock markets in developed and emerging markets unilaterally affected the Chinese stock market, indicating that China was the recipient at this stage. Then, after the outbreak of the Chinese stock market crash, the Chinese stock market had a risk contagion effect on both Japanese and Russian stock markets, indicating that China became a source of financial risk contagion within a limited area at this stage. Lastly, in terms of the degree of risk contagion, the lower tail dependence coefficients of the Chinese stock market and other markets were significantly increased after the occurrence of specific risk events, while the risk contagion degree of developed markets was higher than that of emerging markets. Policymakers can recognize and apply the characteristics of risk contagion at different stages to refrain from unreasonable institutional arrangements, thus improving the sustainability of economic development.

Analyzing the Causality and Dependence between Gold Shocks and Asian Emerging Stock Markets: A Smooth Transition Copula Approach

“…Interestingly, we observe that gold can be used as hedging and non-hedging trading assets for Saudi Arabian and Qatari stock markets during the financial crisis. (5) As suggested by Xu and Gao [8], the occurrence of financial risk contagion can also be captured by the extremely positive co-movement of two financial assets. This concept is consistent with measuring the lower tail dependence between two financial assets.…”

“…The result also suggests strong evidence of the lower tail dependence, but the absence of the upper tail dependence in the cases of BSESN-NGS and KOSPI-NGS pairs, highlighting the importance of contagion and possibly herding during severe contractionary business cycles. According to the financial risk contagion definition, the contagion risk can be viewed as the linkage effect between financial markets caused by systemic risk and, therefore, the lower tail correlation between financial markets can be used to measure financial risk contagion [8]. The results of BSESN-NGS and KOSPI-NGS pairs point to the upper tail dependence with the values of 0.06 and 0.11, respectively.…”

“…The definitions of these two words and their difference are explained in Gulzar at al. [7] and Xu and Gao [8].…”

RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

“…For example, Duan et al (2022) implemented the FRM model through traditional regression analysis methods and enterprise financial indexes and achieved excellent performance (Duan et al 2022). Xu and Gao (2019) analyzed the causality of financial risks based on the nonlinear causality test and dynamic copula method. They effectively analyzed the changes in financial market risks (Xu and Gao 2019).…”

“…Xu and Gao (2019) analyzed the causality of financial risks based on the nonlinear causality test and dynamic copula method. They effectively analyzed the changes in financial market risks (Xu and Gao 2019). Li et al (2021a) introduced new comprehensive analysis indexes into the traditional financial index system based on Hidden Markov Model (HMM), integrated economic statistical structure data, and Internet information.…”