ShanghaiTech SEM Working Paper No. 2018-003
Qiao Yang
ShanghaiTech University - School of Entrepreneurship and Management
Abstract
The study of the joint dynamic behaviour between stock market returns and real economic growth rates is an important empirical question in nance and macroeconomics. This paper investigates their linkage by proposing a vector autoregressive in nite hidden Markov model. Our model has two advantages over the existing approaches in the literatures. In contrast to Markov switching models with xed states, our model will learn the number of states from the data rather than xing it a priori. The vector autoregressive setting in our model allows the joint time series of stock market returns and real growth rates to share the same unobserved state variable. Compared to existing models, our model shows signi cant improvements in out-of-sample density forecast accuracy. This paper demonstrates the predictive power of stock market returns for future growth rates are better captured by the unobserved states variables, rather than the lagged stock market returns.
Keywords: hierarchical Dirichlet process prior, beam sampling, Markov switching, MCMC
JEL Classification: C58, C14, C22, C11
Date Written: April 2016
Available at SSRN: http://ssrn.com/abstract=3159711
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【No. 2018-003】SSRN-2 Stock Returns and Real Growth- A Bayesian Nonparametric Approach.pdf
