This paper studies the role of fluctuation of the aggregate consumption-wealth ratio(cay) for predicting Taiwan stock return. The effect of cay on U.S. stock return has been recently confirmed by Lettau and Ludvigson (2001) with a two stage method. In the first step, estimate the ratio used a dynamic least square(DLS) technique. Second, to investigate the performance of cay, they use in-sample and out-of-sample test. In this paper, we follow the method which Lettau and Ludvigson(2001) use to examine the predictability of cay. Using quarterly market data from 1998 to 2010, we find cay is strong predictors of excess return in out-of sample test. We also find that this ratio is a better forecaster of future returns at intermediate horizons compared to short time.

Although the Black-Scholes (BS) model and its alternatives have been widely applied in finance, their flaws have drawn the attention of many investors and risk managers. The Black-Scholes (BS) model fails to explain the volatility smile. Its alternatives, such as the BS model with a Poisson jump process, fail to explain the volatility clustering. Based on the literature, a novel dynamic regime-switching option-pricing model is developed in this thesis, to overcome the flaws of the traditional option pricing models. Five macroeconomic indicators are identified as the drivers of economic states over time. Two regimes are selected among all likely numbers of regimes under the Bayes Information Criterion (BIC). Both in-sample and out-of-sample tests are constructed to examine the prediction of the model. Empirical results show that the two-state regime-switching option-pricing model exhibits significant prediction power.