TITLE

Forecasting Asset Returns in State Space Models

AUTHOR(S)
Boos, Dominik
PUB. DATE
October 2010
SOURCE
University of St. Gallen, Business Dissertations;10/26/2010, p1
SOURCE TYPE
Dissertation
DOC. TYPE
Article
ABSTRACT
Expectations about the future evolution of the economy are of immense importance for taking the right decisions in a stochastic environment. Econometricians have long been studying forecasting techniques to this end. Most of this work is based on regression techniques such as OLS or GMM. I propose a different approach: state-space models and their estimation by means of maximum likelihood using the Kalman filter. While the two techniques are often identical in an environment with clean data; state-space models are clearly superior if the observed data is affected by measurement error or displays a seasonal pattern. In this case, state-space models allow the separation of the true underlying signal from the measurement noise. As only the signal is relevant for prediction, this can considerably improve the quality of the forecast. In particular, I use the state space framework to estimate affine yield curve models and find that the implied return forecasts for long bonds is much more reliable than that implied by a linear regression, although the implied insample R2 is lower. Moreover, I detect substantial predictability of long/short portfolios not properly revealed by a linear regression. I then generalize the affine yield curve models such that they can include persistent shocks or state variables not spanned by yields. Firstly, these unspanned factor models are used to further improve the yield-curve forecast by including expected inflation as an additional state variable. In this model, the R2 of the annual term premium forecast is above 30 percent. Secondly, I build a joint stock-bond model that merges the yield curve model with a stock market model using the price-dividend ratio as an additional variable. This is achieved by linearization using the Campbell-Shiller approximation. Thirdly, the cross-section of assets is enlarged by including size and book-to-market sorted portfolios. This model provides evidence for substantial variation in the dividend growth rate. Once the model captures this feature, it is able to explain a large fraction of the value premium by a higher exposure of value stocks to the single persistent shock of the system. Finally, this thesis uses rank-reduction techniques to explore the return predictability pattern. This analysis provides strong evidence for at least two independent predictability factors: the term premium and the equity premium.
ACCESSION #
82752537

 

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