FULOP Andras

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This doctoral dissertation consists of three essays in financial asset pricing, with a particular focus on the predictability of U.S. Treasury bill returns. In the first essay, we study the statistical and economic evidence for the predictability of bill yields in real time for a Bayesian investor who becomes familiar with the parameters, states, and models over time. In the second essay, I study bond risk premia under predictive systems. In the third essay, I study the power of stock investor sentiment to predict bond returns.

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We develop a new model where the dynamic structure of the asset price, after the fundamental value is removed, is subject to two different regimes. One regime reflects the normal period where the asset price divided by the dividend is assumed to follow a mean-reverting process around a stochastic long run mean. The second regime reflects the bubble period with explosive behavior. Stochastic switches between two regimes and non-constant probabilities of exit from the bubble regime are both allowed. A Bayesian learning approach is employed to jointly estimate the latent states and the model parameters in real time. An important feature of our Bayesian method is that we are able to deal with parameter uncertainty and at the same time, to learn about the states and the parameters sequentially, allowing for real time model analysis. This feature is particularly useful for market surveillance. Analysis using simulated data reveals that our method has good power properties for detecting bubbles. Empirical analysis using price-dividend ratios of S&.P500 highlights the advantages of our method.

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