Empirical Analysis Of Capital Asset Pricing Model

Financial variables has characteristic of asymmetric and time varying In financial market which complementary and substitution each other. Here we examines the influence of Macroeconomic variables on stock market returns in emerging stock market, in P. R. China with cointegrated vector autoregressive Markov-switching model(MS-VECM). Also bootstrap simulation is used to test the signification of parameter estimated. The results is three states which is determinant:(1) the developing early stage (state 1); (2) macroeconomic is healthy and stable development (state 2); (3) the financial market is influenced by macroeconomic policy. Fisher hypothesis only exists in mature stage. We found (a) exchange rate has positive effect about return of stock index; the comsumer price index, (b) the money supply have different affect on stock return in different regime. (c) free risk rate and Standard & Poor 500 index have negative effect in early stage and positive effect in mature stage on stock returnSecondly an present-value model is used as the basic model, to analysis the unobserved variable-speculative bubbles of Shanghai stocks index and Husheng300 index and mid and small capital stocks index. state space model is used to and the parameter is estimated by kalman filter. Per-share earnings is proxy of dividends per share in this paper. The bubbles is tested whether existed or not, and its character such as size and formulating speed, the period is also analyses. The result is that the stock index can't be explained by market foundation value. The stock index is positive bubbles in a long period and cumulative a long time. But its deflated abruptly also. Then negative bubbles existed markedly short-lived. Mid and small capital stocks index is more speculative bubbles than others. The discount factor estimated show that the bubbles can't sustain for ever and will collapse.Then dynamic and time varying of capital asset pricing model (CAPM) is considered. CAPM is analysis using Bayesian dynamic linear model, then extended to panel data Bayesian dynamic linear model. However, the variance matrix of both measure equation and state equation are unknown. Markov chain monte carlo and gibbs sampling technology is used to estimate two variance arrays. The smooth filter is used to estimate all time varying parameters of panel data in CAPM. The results is beta which is risk premium factor, of commercial bank have lower volatility than that of non-commercial bank. Risk premium factor beta In up market, in CAPM is positive in long time and negative in downside market and zero mean value.Finally, extreme value or value at risk is researched. In the chapter, CAPM is analysis by panel data quantile regression. The beta in different quantile which estimated by panel quantile regression is compared with which estimated using fixed effect panel data. The beta value which estimated by fixed effect panel data is higher than that of 0.6 quantile. The traditional test such as t test can not be used to test panel data quantile regression, so bootstrap simulation also be used to test significant of parameter. The beta value which estimated by panel data is higher than that of 0.6 quantile. Because of financial variable which is asymmetric of, the beta value is getting bigger accompany with growing quantile. The distribution of financial variable is not symmetry.The contribution of this dissertation is:(1) the stock return is influenced by which maroeconomic variables and how to be influenced; (2)the speculative bubble is measured with dynamic linear model in China stock market; (3)the time varying and asymmetric parameter of CAPM is analysis using panel data Bayesian dynamic linear model;(4) the difference quantile beta is analysis using panel data quantile regression.