Three essays on the effect of learning and predictability on optimal dynamic portfolio strategies and asset prices

The first essay studies the effect of learning on equity premium and stock price volatility. A dynamic general equilibrium model of stock prices is developed which yields a stock price volatility and equity premium that are close to the historical values. The non-observability of the growth rate of the dividend process introduces an element of learning into the stock valuation process which is shown to increase the volatility of the stock price for realistic parameter values. The second essay examines the effects of uncertainty about the predictability of stock returns on optimal dynamic portfolio choice in a continuous time setting with a long horizon. Uncertainty about the predictive relation affects the optimal portfolio choice through dynamic learning, and leads to a rich set of relations between the optimal portfolio choice and the investment horizon. The third essay addresses the asset allocation problem faced by a long-horizon investor in a world with stochastic real risk free rate and stochastic inflation. The investor hedges both real risk free rate and inflation risk with nominal bonds. The optimal hedge demand is analytically derived and comparative static analysis is carried out. The model explains the apparent inconsistency between the Tobin Separation Theorem and the advice of popular investment advisors pointed out by Canner et al (1997).