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A dynamic analysis of stock markets using a hidden Markov model
Authors:Luca De Angelis  Leonard J. Paas
Affiliation:1. Department of Statistical Sciences , University of Bologna , Bologna , Italy;2. Department of Marketing, Faculty of Economics and Business , VU University , Amsterdam , The Netherlands
Abstract:This paper proposes a framework to detect financial crises, pinpoint the end of a crisis in stock markets and support investment decision-making processes. This proposal is based on a hidden Markov model (HMM) and allows for a specific focus on conditional mean returns. By analysing weekly changes in the US stock market indexes over a period of 20 years, this study obtains an accurate detection of stable and turmoil periods and a probabilistic measure of switching between different stock market conditions. The results contribute to the discussion of the capabilities of Markov-switching models of analysing stock market behaviour. In particular, we find evidence that HMM outperforms threshold GARCH model with Student-t innovations both in-sample and out-of-sample, giving financial operators some appealing investment strategies.
Keywords:stock market pattern analysis  regime-switching  hidden Markov model  financial crises  market stability periods
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