Analysis Of The Correlation Between Liquidity And Yield In China's Stock Market

Liquidity,one of the attributes of financial products,reflects the efficiency and effectiveness of financial market.Extensive and advanced research papers concerning relationship between liquidity and return of asset are in increasing number since Amihud discovered the liquidity premium effect in 1986.Meanwhile,more and more investors are convinced that the profits of their portfolio would be somewhat associated with the liquidity level or risk of the assets.Traders and the regulators are astonished by the shocking Dow-Jones Average Plunge in 1987 and2010,respectively,wild swings caused by the fat-finger of the Guangda Securties in2013,stock crash of China in 2015 as well as the steep fall triggered by the circuit breaker in the beginning of 2016.Under such circumstance that financial crises were accompanied with the liquidity-dry-up,scholars proceed to analysize the the relationship between the liquidity and the return.This paper aims at investigating the correlation between the liquidity and return of China's stock market,including:(1)estimating time-varying Spearman coefficients with DCC-GARCH model and Copula model under the different sample intervals and different moving window length;(2)summarizing the statistics attributes of the dynamics coefficients and trying to the figure out the certain implications for the trend of stock market;(3)comparing the above two models based on the theoretical analysis and empirical test.Shanghai Stock Exchange Component Index from January of 2005 to December of2015 serves as research objectives,with 2671 trading days in total.Amihud illiquidity and difference of logarithmic closing prices,are conducted as the proxy for,liquidity and return,respectively.This paper proceeds as follow.Initially,DCC-GARCH-t(1,1)and SJC-Copula-GARCH-t(1,1)are identified as the best models out of others based on the maximum likelihood and minimum squared euclidean distance.Time-varying correlations then are estimated under total sample,different moving window length(60days,120 days and 250days)and bull and bear markets.Eventually,there are summary of the statistics attributes of dynamics correlation and its role in the prediction for the tendency of the market as well as the comparison between the two models.The empirical evidences demonstrate that:(1)in average,illiquidity is negatively associated with return;(2)the dynamic coefficient transcends the zero zones and turns into positive after the beginning of bull market,while reverses into negative again before downturn.In other word,the positive and negative turn reversal of the dynamic coefficient plays a certain role in the prediction for the tendency of the stock market;(3)the correlation declines into the minimum in the bear market;(4)the upper tail dependence between the illiquidity and return is far more larger than the lower tail dependence;(5)the volatility of time-varying correlation is rather significant at the beginning while mirrors decay trend in last phase of the sample;(6)the dynamic correlation is easiest to captured under window length of 120 days;(7)SJC-Copula eclipses the DCC-GARCH in observation number constraint,the sensitivity in predicting the trend of market as well as the smooth.There are three innovations underlined in this paper:(1)the positive and negative relationship between the illiquidity and return are compared into the honeymoon effect and divorce effect which is popular mentioned in the field of international finance;(2)both DCC-GARCH and Copula models are applied to estimate the time-varying Spearman coefficient;(3)based on the statistics characteristic of the time-varying coefficient,the indication of correlation to the tendency of the market is summarized in the end the paper.To sum up,the first point is the innovation in theory,the second one is the innovation in methodology while the last one is the innovation in content.