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1、12700 單詞, 單詞,14500 英文字符 英文字符,4000 漢字 漢字出處: 出處:Hau L C. Stock Market and Consumption: Evidence from China[J]. Berkeley Undergraduate Journal, 2011, 24(3).STOCK MARKET AND CONSUMPTION: EVIDENCE FROM CHINASection II. Liter

2、ature Review: Stock Markets and Consumer SpendingA. The Stock Market “Wealth Effect”The traditional economic theory suggests that stock market returns change the wealth of the investors which subsequently and directly af

3、fects their spending. According to Deaton (1992), a stock market boom increases investors’ consumption, while a stock market crash causes a slowdown in economic activities such as consumer spending. Poterba (2000) furthe

4、r suggests that the stock market wealth effect would be strongest and most obvious among the small set of households who own the majority of corporate stock. In contrast, this effect for the other households should be mo

5、dest. This is supported in most empirical studies in developed countries, which showed a positive correlation between stock price and macro-economic growth rate. For example, Johansen (1990) found a long-term equilibrium

6、 relationship between the securities prices in the U.S.A. stock market and macroeconomic variables. Studies on this linkage in developing countries are, however, non-conclusive. Funke (2004) found a small, but statistica

7、lly significant, link between private consumption growth and stock returns in most developing countries. In contrast, Harris (1997) showed that the effect of stock market on economic growth in developing countries is har

8、dly observed.3C. The ARCH/GARCH ModelIn order to estimate the Shanghai Stock Exchange volatility, it is essential to introduce the GARCH model here. In the domain of finance, data of time sequence usually suffers from he

9、teroscedasticity in which the error terms and variances are expected to be larger or smaller for some points than the others. Although the regression coefficients for an ordinary least squares regression are still unbias

10、ed, the standard errors and confidence intervals estimated will be too small and not precise. Engle (1982) puts forward the Autoregressive Conditional Heteroscedasticity (ARCH) model which treats heteroscedasticity as a

11、 variance to be modeled. As in Equation 1 below, the theory postulates that the conditional variance of the t-th term is a function of the residual error from the (t-1)-th term to the (t-q)-th term, i.e. the fluctuation

12、is self- correlative, and the form of ARCH (q) becomeswith the non-negative condition that .The theoretical advancement in the domain of conditional heteroscedasticity devel

13、oped very quickly and many variations of ARCH model have been proposed. The most representative model is the generalized ARCH (GARCH) model proposed by Bollerslev (1986). In the GARCH model, the conditional variance depe

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