The Jump Spillover Relationship Between Stock Market And Bond Market In China

The development trend of China’s financial market is gradually on the right track,at the same time through the opening of the bonds,let China’s bond market will be included in the main global bond index,at the same time,domestic credit risk caused by lots of bond defaults in the more events are more.The unpredictability of future bond markets will undoubtedly increase.The bond market and the stock market occupy the most important position in the financial market of our country.Therefore,in this situation,the need for in-depth understanding of the correlation between the two markets is increasing.Based on the perspective,it is clear that the transmission mechanism and mutual relationship between China’s stock market and bond market can provide a favorable theoretical basis for the future development of financial policy in China.Therefore,this paper selected data from 2013 to 2017,based on the Shanghai stock index and trading data of Shanghai bond index with its representing our country stock market and bond market,in order to jump dependent stochastic model(SVCJ)stock of debt in our country is studied on the basis of jump spillover effect of two market’s earnings.We chose a relatively complex model to further explore the relationship between China’s stock market and the bond market,additional,adding jumping items in the random fluctuation model.The model has also been shown to be more effective in describing jumping behavior in the stock market.By using the markov model and carlo simulation method(MCMC algorithm)to identify the hidden process in the volatility jump of the yield sequence,and carrying out the related parameter estimation.At the same time,two indexes are selected to analyze and characterize the jumping items.The indexes are conditional jump overflow probability and jump overflow strength respectively.In addition,it also studies the introduction of risk events in the jump overflow relationship.Based on this perspective,explore the cause of market jumping.Among them,the risk event mainly refers to some important events with sudden,influential and uncontrollable factors.Events such as political events,sudden crises,and geological disasters.From the method of empirical research,we consider MCMC algorithm to estimate parameter based on bayesian theory,which can solve relatively complex high-dimensional distribution efficiently and accurately.The principle is based on combining priori information and observation data through bayesian algorithm,it also obtain the unestimated posterior distribution.In the second process,we chose a relatively simple Gibbs sampling in the empirical study to recognize the simulation of the model.What the Empirical results is: there is a significant jump spillover effect between China’s stock market and bond market.At the same time,it is found that the jumping strength of the stock market is higher than that of the bond market.But the bond market has become the dominant market in jumping behavior if the event of a total jump from both markets.At the same time,by comparing the occurrence of the risk events with the jumping time,it is found that they are highly correlated.For micro level,the main contribution of the SVCJ model based on MCMC algorithm under through total jump jump to study different market interaction,this article through the study and the risk of incident Angle of view of the mutual influence of two markets,this also for investors portfolio and risk management provides a theoretical guidance.On a macro level,and many times is quite different from the results,the influence of the macro policy and risk events develop or implement related macro policy will be different extent with the stock market and bond market volatility even jump and due to the policy under the influence of the interaction between the two cities.If we can understand the relationship between the two cities,the policy formulation and the policy implementation effect can provide reliable advice.At the same time,it can help us to understand more comprehensively how the results of the excessive coordinated movement of different capital markets in the financial crisis are generated and transmitted.