Tail Dependence Analysis Between China’s Stock Market And World’s Major Stock Markets

Financial globalization challenges the stability of a country’s economy. With the strength ofthe international economic integration, with the rapid development of communication technology,with the increase of the type of financial derivatives, the flow of funds between the internationalcountries gradually deviates from the international trade, forming a more and more hugeinternational financial market.Tail correlation analysis between China’ stock market and the world’s major stock marketduring the crisis reflects the degree of dependency between China’s stock market and the world’smajor economies’ stock market; Besides, it reflects that when a crisis broke out, the conditionalprobability of China’s stock market received a contagious; ultimately it reflects the transmissionchannels of the crisis. Tail risk is the key of risk control theory, and the tail correlation analysis isan important part of the successful risk management.Traditional economic theory assumes thatthe variable is always belong to the normal distribution, lognormal, and take linear correlationfunction as a measure of risk indicator. However, a large number of studies have shown thatfinancial markets are complex, and some form of fixed function can’t accurately describe themarginal distribution. As financial time series have fat tail and the time-varying nature ofdeflection, therefore a linear correlation function can’t capture the characteristics of the tail, andthere is a big deviation. Besides, we can use the extreme theory to calculate the value at risk whichbased on the POT model, but it has limitations on capturing the correlation of the tail betweendifferent stock markets. Bivariate extreme value, which based on the univariate extreme, has madefor the disadvantage of the univariate extreme value. Bivariate extreme theory can be used tostudy the tail dependence between different stock markets and test if it is relevant when it comesboom and slump.This article will use the bivariate extremes method which can include the information of abinary random variable inter dependence. First, the POT model, which simulates the superthreshold sample distribution that based on the rate of return of unknown distribution, can formthe GPD and calculate the VaR and ES at the confidence level of90%. Then, test it back. The result shows that the VaR and ES that calculated through the GPD method has lower failure rate.Then, this article constructs two test statistics. They are as follows: the likelihood ratio teststatistic and the Score test statistic. If the tail dependence correlation coefficient between China’sstock market and the world’s major stock markets is significant, we use "Y" to represent; If the taildependence correlation coefficient between China’s stock market and the world’s major stockmarkets is not significant, we use "N" to represent, and these are the qualitative analysis. Theresult shows that the tail dependence between China’s stock market and the America’s stockmarket, the France’ stock market, the England’ stock market, the Japan’s stock market, theHong Kong’ stock market is significant. However, the tail dependence between China’s stockmarket and the German’s stock market is not significant. Secondly, combined with bivariateextreme, we can calculate the strength between the stock tail dependence and calculate thecorrelation coefficient. Keduall rank correlation coefficient τ and Spearman rank correlationcoefficient detect these results. These are quantitative analysis. The results prove that the taildependence between China’ stock market and the France’s stock market is stronger than the others,and the correlation coefficient is0.8044855. The tail dependence between China’ stock market andthe German’s stock market is weeker than the others, and the correlation coefficient is0.496065.In all, the results between the quantitative analysis and the qualitative analysis are consistent. Thelower tail correlation coefficients between China and France, Germany, Hong Kong, the UnitedStates are greater than the upper correlation coefficient, indicating that correlation coefficientsincreased significantly during the crisis. It means that when comes across a bear market, theprobability of Chinese stock market and the four economies’ stock markets slump at the same timeis much larger than the probability of Chinese stock market and four economies’ stock marketboom at the same time when it comes the bull market. However, the upper correlation coefficientand the lower correlation coefficient between China’s stock market and Japan’s stock marketdidn’t change much, indicating that the tail dependence between China’s stock market and Japan’sstock market didn’t change much. While, the coefficient of tail dependence between China’s stockmarket the Japan’s stock market is still larger than other economies.Bivariate Extreme method that used to study tail dependence between China’ stock marketand the world’s major economies’ can not only to deepen the understanding of the microstructure of financial markets, but also improve the accuracy of financial risk measure. Therefore, it hascertain practical meaning and theoretical value.