Research On The Relationship Between Stock Market Stability And Monetary Policy

The stock market in China has developed for about 20 years. By the end of 2009, the aggregate market value of Shanghai and Shenzhen stock exchanges reached 73 percent of China's GDP, all these show that stock market is having an increasing impact on people's daily life and wealth. Thus, the persistent prosperity and stability is vital to national well-being and the people's livelihood. In recent years, though China's stock market is expanding at a rapid pace and its operation system is improving, it still has not broken away from the shadow of policy market, where the direct influence of monetary policy is of great significance. Furthermore, in addition to its own fluctuation, stock market also has an influence on central bank's monetary policy decision.This dissertation firstly analyzed factors that influence fundamental stock value and market price as well as the general rule of stock price fluctuation, and it finds that the fluctuation of assets price is not completely pushed by fundamentals, but also influenced greatly by money side and investor behavior. Then this dissertation further explored the influence of stock market fluctuation on monetary policy so as to analyze the inherent mechanism and illustrate that monetary policy should not ignore stock market fluctuation. This dissertation also conducted an empirical study on the relationship between stock market and money demand variables including M1, M2. On the other hand, the influence of monetary policy on stock market fluctuation was also discussed, and two variables including money supply and interest rate were considered when analyzing the stock price. By comparing two opposite points, this dissertation shows that governments should consider how to deal with stock price fluctuation when making monetary policy decision. At present, especially at the stage of post financial crisis, the authorities should carry out cautious and flexible measures to balance between economic overheating and economic recession.Besides, Granger Causality Test was done between assets price and credit based on both quarterly data and monthly data, then, the influence of stock market on money side was also researched. By combining empirical results and the history of China's stock market, this dissertation discussed the optimal path for balancing stock market stability and monetary policy, and finally proposed some effective advices. meanwhile, we employing the GARCH type models with Generalized Error Distribution, based on the week data of stock index of Shanghai from Jan 1991 to June 2010, studied the volatility clustering characteristics of Chinese stock makert return. We intruduce a nonparametric function which reflects the tendency in the longrun of stock makert return, after filtered the tendency, our models explain the facts very well.The general idea of this dissertation is:Firstly, it briefly introduced the stock market background and important events both at home and abroad; Secondly, a literature review was done so as to seek the breakthrough point; Thirdly, based on some basic principles, an empirical study was conducted to explore the relationship between stock market and monetary policy; Fourthly, conclusions and policy implications were proposed, and further study direction was also mentioned.The innovation points can be summarized as follows:Firstly, Granger Causality Test was conducted between variable credit/GDP and variable rate of stock return by using quarterly data and monthly data, meanwhile, cointegration theory was also used to study China's money demand function as well as the influence of stock market on money demand; secondly, this dissertation explained the related influence of 2008 global financial crisis on macroeconomy, monetary policy, stock market and real estate market, which would be more practical and persuasive. Thirdly, we intruduce a nonparametric function which reflects the tendency in the longrun of stock market return when we study the volatility characteristics of stock market, the new model explains the volatility clustering characteristics very well compaired to those traditional models.