Risk Dependence Analysis Of Stock Markets

The motivation of financial risk is the loss or gain of the financial assets caused by many uncertainties changes for an implied risk factor. The development of financial institutions is subject to many risks in the restrictions. In order to better predict and control the risk, the researchers and financial institutions design a number of risk measurement methods, in which VaR is used most often. However, based on the assumption that the gains of a single capital are normal, the income linear dependent calculation of VaR in the risk assets does not match the actual situation. The emergence and application of Copula theory for risk analysis and multivariable time series analysis provide a new direction. Copula is used to describe the correlation in financial market structure, not only can choose a better description of the gains of risk capital distribution function, but also can strip out the correlation in financial market structure, more fully describe the level of dependencies in the risk assets.The innovations of this paper are as follows:First, the paper has realized to study on the dependence relations between random variables based on copulas, an instrument which is used in this paper as an innovation, in terms of determining the nature and quantification, resorting to the descriptions of dependence properties and association strength and, by way of which, broke through the limitation of study with only depending on independence and linear dependence.Second, the paper has broadened the spread of dependence relations dependence relations between random variables. Furthermore, the measure precision has been improved wholly and partly. Sample covers the major stock market price index, the relationships between which are analyzed in this paper respectively. The sample has long time span, and the member of each data group are over 1000.Third, this paper adopted contrastive study innovatively. Through theoretical analysis and empirical research, this study explored capability difference between two types of copula functions-Clayton Copula and Gumbel Copula—as instruments to capture the tail correlation between two series of financial data and to describe the structure of financial data Tail Dependence under different market conditions (rise and fall). The main content and basic structure are as follows:Chapter 1 is the introduction. Comparing with the drawback of the existing risk quantitative models, the paper point out advantages and practical significance of Copula function on the application of risk dependence research, and describes the overall research methodology and innovation, as well as the structure of the paper.Chapter 2 is literature review, with a focus on the research results of Copula theory in the financial field. After a definition of Stock Market Risks, the paper gives a detailed explanation on evolution and application of home and abroad study, from the birth of Copula theory to the application in the analysis of the relevance of financial field.Chapter 3 introduces the definition and the nature of Copula theory, expresses the distribution function and density function of several commonly used Copula function, and gives the definition and reasoning of some relevance indices based on Copula theory.Chapter 4 is tail correlation analysis of stock markets. In this part, the paper establishes Copula-GARCH(1,1)-t Model and uses parameters of Copula toexplore the Tail Dependence coefficient and quantile-dependent coefficient between Shanghai Composite Index and Shenzhen Composite Index, Hang Seng Index and, Dow Jones Industrial Average Index, respectively. The results show that the Shanghai Composite Index has significant relationship with Shenzhen Composite Index, Hang Seng Index and, Dow Jones Industrial Average respectively. Moreover, the correlation between the Shanghai and Shenzhen Composite index is most significant; and the correlation between Shanghai Composite index and the Dow Jones Industrial Average Index is the minimum.Chapter 5 is comparative analysis of the Tail Dependence. This paper selected two contrasting movements of the time interval of the three groups of structural comparative study of samples to explore the two most commonly used in the Copula function in different market conditions to capture financial data related to the ability of the rear differential.Chapter 6 is conclusions and policy recommendations. This part gives specific policy recommendations based on the findings.