| A dependence study for different random variables is carried through from the view of risk analysis and the method for dependence study is introduced. It is well known that the coefficient is a measure of linear correlation. To measure dependence through computation of correlations reveals adequate in the context of multivariate normally distributed risks or in assessing linear dependence, but it is not appropriate to measure the nonlinear dependence withÏij. The concordance measure Kendall's tau is presented which has no limitation for the distribution of random variable. Being affected by other factors, simple correlation coefficients often indicate the nonessential relationship between variables. To obtain the essential relationship, the concept of partial correlation coefficient and the method of calculation are presented in this work. For a risk manager, it is not enough to know the relationship between different random variables only. They want to know more, such as the interactional relationship between variables. The Granger causality test is introduced and the application is presented with an example. If regression is carried for nonstationary time series, the spurious regression will be produced. To resolve that, the method of cointegration is introduced. Eventually, to obtain more information about dependence relationship and quantitate risk, the concept of copula and the estimate method of copula function are introduced and applied to calculate the tail dependence. The tail of random variable's distribution is the focus of the risk analysis because the event with small probability often occurs in this area. The concept of tail dependence is introduced here to analyze the tail risk of random variable. Since copula function contains all the information of tail dependence, it can depict the tail dependence relationship between variables comprehensively. In the last section of this paper, an example is presented to demonstrate the application of the method mentioned above.
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