The Integrated Empirical Analysis For Shanghai And Shenzhen Stock Markets Of China

Financial data often have heavy-tailed phenomenon. Since in the 1960's, there has accumulated a great deal of empirical evidence for heavy tailed models in finance. In these models, the probability of a large fluctuation falls off like a power law. The generalized central limit theorem shows that these heavy-tailed fluctuations accumulate to a stable probability distribution. If the tails are not too heavy then the variance is finite and we find the familiar normal limit, a special case of stable distributions. Otherwise the limit is a nonnormal stable distribution, whose bell-shaped density may be skewed, and whose probability of large fluctuations diminishes. A smaller value ofαmeans that the probability tails are heavier, implying more volatility. In fact, whenα< 2 the theoretical variance is infinite. A portfolio can be modeled using random vectors, where each entry of the vector represents a different asset. The tail parameterαusually depends on the coordinate. Modeling this kind of data requires choosing the right coordinates, estimatating the tail index for those coordinates, and characterizing dependence between the coordinates. This paper makes portfolio modeling with heavy tailed random vectors by using these methods. And this paper makes a real example analysis for the integrated between Shanghai and Shenzhen stock markets by using the modeling. The results show that the two stock markets closely relate to each other and the interaction is great.