A dynamic analysis of stock markets using a hidden Markov model

Luca De Angelis and Leonard J. Paas

Journal of Applied Statistics, 2013, vol. 40, issue 8, 1682-1700

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.

Date: 2013
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DOI: 10.1080/02664763.2013.793302

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