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Languages : en
Pages : 131
Book Description
My dissertation studies the asset pricing implications of non-linear models, including regime-switching models and non-linear diffusion models. The first chapter investigates empirically the effects of regime switches in stock returns and volatilities. First, the empirical results suggest that the expected excess return and the volatility are the monotonically increasing functions of the investors' belief. It implies that risk aversion is time-varying and the representative agent is more risk averse in the bear regime so that higher expected excess return and higher volatility are generated in the bear regime. The empirical work also finds that the term spread, the inflation rate, and the T-bill rate have significant business cycle patterns in the predictive regressions. For example, the term spread is positively related to the stock market returns in the bull regime, but is negatively related to the stock market returns in the bear regime. This suggests that the increasing term spread is a good news in the bad regime because it indicates that the economy is improving and will recover soon, thus the investors require a lower equity premium. In the second chapter, an econometric method is developed for pricing and estimation for a newclass of non-linear diffusion processes. These type of non-linear diffusion processes are used to model the dynamics of the VIX index under both the objective measure and the risk-neutral measure, where the latter is estimated from futures prices. The difference between the drifts under the objective measure and the risk-neutral measure is defined as a measure of the variance risk premium. The predictive regressions demonstrate that the variance risk premium estimated by the non-linear diffusion models has stronger predictive power for stock returns than the affine models. In the third chapter, a hidden Markov model is used to describe the dynamics of the realized variance of stock market returns. I investigate the relations among the variance regime, variance risk premium, and stock market returns. I find that the variance risk premium, i.e., the difference between the expected return variation under the risk-neutral and the physical measures, is higher in the high-variance regime, in which the volatility-of-volatility is high. However, the positive relation between the variance risk premium and future stock returns is entirely due to a component of variance risk premium that is orthogonal to the current realized variance and the variance regime. The results suggest that the predictive power of the variance risk premium for stock returns is more likely due to its correlation with time-varying risk aversion than with time-varying risks.
