Analysis Of Financial Markets Correlation Based On Vine Copula Model

In the 21st century where information is continuously shared at a high speed,the large increase of volatility and unstable behavior in financial markets and the different degree of interdependence between financial markets inevitably bring about a series of interlocking financial risks.Venture investors in the financial field have always been committed to the allocation of excellent asset portfolios,the key is to explore the different degree of correlation between financial markets,select weak correlation or even unrelated markets or assets to optimize the portfolio,reduce portfolio risks.Financial markets usually have obvious characteristics such as thick tail spike and heteroscedasticity,which are greatly different from the normal distribution.Moreover,there is a nonlinear correlation between financial markets,which makes it difficult for the traditional correlation measure method used to describe the linear correlation between random variables to accurately describe the degree of correlation in the financial field.The famous Sklar theorem allows you to use copula and edge distributions to build multivariate distributions,and you can model the dependencies between different elements in a portfolio without knowing the distribution of variables.This theory makes more and more scholars devote themselves to using copula function to measure the correlation between financial markets.With the deepening of the research,scholars have also found that it has many shortcomings,such as it can only be used to measure two markets,etc.When the correlation between multiple markets needs to be studied,vine copula function has to be further introduced.In the empirical part of this paper,the GARCH(1,1)-t model is used to determine the edge distribution of each sequence,and the copula model is used to fit the new sequences generated by probability integral transformation.Through AIC and BIC tests,the following conclusions are drawn: First,when examining the Shanghai and Shenzhen stock markets,the fitting effect of t copula function is the best,indicating that there is a strong correlation and tail correlation between the Shanghai and Shenzhen stock markets,indicating that the risk degree of the two markets in the face of extreme events is consistent;Secondly,according to the fitting effect analysis of vine copula,D vine copula is more suitable than Canonical vine copula to fit the correlation between multiple financial markets.Since there is no principal variable in the D vine structure,it indicates that there is no dominant financial market in the D vine structure,and each financial market has the same weight.With the increase of the layer tree of the tree structure,there are more and more common information as known.The more common information is known,the less correlation there is between markets.It shows that in the context of global financial sector,the more common information is known,the weaker the correlation between the two markets.Some suggestions are put forward: when investors make investment strategy,they must grasp the relevant information of the market in time and be familiar with the influence of macroeconomic policy.At the same time,the risk and return of each market asset are quantified,and the appropriate asset portfolio is selected to achieve the purpose of reasonable risk avoidance.In China’s financial market,there is a strong correlation between assets in the same market.For example,assets in the Shanghai and Shenzhen stock markets have a strong correlation and tail correlation,while cross-market assets such as stocks,gold,foreign exchange and bond markets have a weak correlation.Therefore,when allocating investment strategies,in order to avoid the risks caused by the strong correlation of assets in a single market,multi-market asset portfolios can be carried out from the perspective of asset allocation in large categories.