Infinite Hidden Markov Models with Application to Speculative Bubble Detection
Dr Yong Song, Business School; University of Technology Sydney
30th Aug 2013 11:00 am - Room 498 Merewether Building H04
This paper proposes an infinite hidden Markov model (iHMM) to detect, date stamp, and estimate speculative bubbles. Three features make this new approach attractive to practitioners. First, the iHMM is capable of capturing the nonlinear dynamics of heterogeneous bubble behaviors as it allows an infinite number of regimes. Second, the implementation of this procedure is straightforward as the detection, dating, and estimation of bubbles are done simultaneously in a coherent Bayesian framework. Third, the iHMM, by assuming hierarchical structures, is parsimonious and superior in out-of-sample forecast. This model and its extensions are applied to the price-dividend ratio of NASDAQ Composite Index from 1973M02 to 2013M01. The in-sample posterior analysis and out-of-sample prediction find evidence of explosive dynamics during the dot-com bubble period. Model comparison shows that the iHMM is strongly supported by the data against the finite hidden Markov model.