The Research On Jump Behavior Of CSI 300 Index Returns Based On Dynamic Jump Model

Along with the intensification of the global financial market volatility, returns on assets to large-scale changes in the frequency are getting higher and higher. The assets in these markets enjoy more obvious double features of normal volatility and jump. Therefore, the research upon the jump risk of asset returns has already become one of the important and difficult issues in financial field. Especially, the developing countries including China and some emerging countries, which boast a short history of financial development and relatively imperfect financial system, have fewer opportunities to join in formulating the international financial principles and are unfamiliar with the emerging financial instruments, are more like to be hit by international financial risks and suffer more losses.In order to learn the abnormal return of the Chinese stock market, we consider the Poisson jump diffusion process trying to capture the jump behaviors of the CSI 300 Index. The traditional Jump-GARCH model has two defects, one is it didn’t consider the asymmetric reaction of asset returns according to information, the other is didn’t take the aggregation phenomenon of jump into account. So, we use the EGARCH model instead of the GARCH model to describe the volatility, in order to emphasize asymmetric effects of good news and bad news, meanwhile, we use Poisson autoregressive jump intensity to describe the jumps, in order to emphasize Jump behavior is time-varying and clustering, and last jump would influence the normal volatility.First, we make maximum likelihood estimation model parameters of by using genetic algorithms, and to explain the economic meaning of those parameters. Second, we use error sum of square testing and likelihood ratio testing to compare dynamic jump model, and select the model witch can most fitting CSI 300 index jumping behavior. Third, we analysis the jump behavior of CSI 300 Index accounting to statistical characteristics、accounting analysis and events. Last, we introduce a dummy variable, which is CSI 300 index futures, into the dynamic jump model, for analyzing the changes in CSI 300 index jump behavior after releasing the index futures.The empirical results show that the past overall disturbance of CSI 300 index returns has asymmetric effect on volatility. Index exists jumping behavior, and jump intensity is time-varying and has obvious aggregation. The jumping behavior that has occurred will affect the normal volatility of the next issue, and jump volatility take a large proportion of the overall conditional volatility. When CSI 300 index futures is not appearing on the market, the jump of CSI 300 index returns is more frequently, and jump amplitude is larger. The introduction of stock index futures reduce the proportion of jumping portion on index volatility, and slowing the jump strength of the underlying index volatility, and make the asymmetric effect of good news and bad news become smaller. So the introduction of CSI 300 index futures has recused the jump risk of underlying index.