- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Many researchers have realized that there is a strong correlation between stock prices and macroeconomy. In order to make this relationship clear, a lot of studies have been done. However, the casual relationship between stock prices and macroeconomy has still not been well explained. A key point is that, most of the existing research adopts linear and stable models to investigate the correlation of stock prices and macroeconomy, while the real causality of that may be nonlinear and dynamic. To fill this research gap, we investigate the nonilnear and dynamic causal relationships between stock prices and macroeconomy. Based on the case of China's stock prices and macroeconomy measures from January 1992 to March 2017, we compare the linear Granger causality test models with nonlinear ones. Results demonstrate that the nonlinear dynamic Granger causality is much stronger than linear Granger causality. From the perspective of nonlinear dynamic Granger causality, China's stock prices can be viewed as "national economic barometer". On the one hand, this study will encourage researchers to take nonlinearity and dynamics into account when they investigate the correlation of stock prices and macroeconomy; on the other hand, our research can guide regulators and investors to make better decisions.
This paper explores the linear and nonlinear dynamic correlation between China's stock prices and macroeconomy. The results show that the nonlinear Granger causality between China's stock prices and macroeconomy is stronger than the linear Granger causality between them. Compared the static nonlinear Granger causality test method with the dynamic nonlinear Granger causality test method, it can be known that stock prices have the functions of “national economic barometer”. The results of the study can provide decision support to regulators and investors.
The widely used linear Granger causality test method describes whether the historical value of one variable is linearly related to the present or future value of another one. Though it has an advantage that the results are intuitive and can be well explained by economic theories, it is difficult for us judge the size or the sign (positive or negative) of the coefficients. Compared with the linear Granger causality test method, the nonlinear Granger causality test method can extract the complex nonlinear relationship between variables more comprehensively and accurately, but economic meaning of the nonlinear Granger causality is not as intuitive as before (see e.g. ). Therefore, our research is suitable for find economic clues, while the explanation of our discoveries by economic theories needs to be improved in a further step. The following are some promising future work:
(1) To improve the existing nonlinear Granger causality test method, so that it has a more clear economic implications.
(2) The conclusion that China's stock prices are barometer of national economy is only established in the sense of Nonlinear Dynamic Granger Causality test, and its specific mechanism needs to be studied.
(3) To study the actual performances and consequences of the conclusion that nonlinear Granger causality between China's stock prices and macroeconomy is stronger than the linear Granger causality between them.