A dynamic analysis of stock markets using a hidden Markov model

L. de Angelis, L.J. Paas

Research output: Contribution to JournalArticleAcademicpeer-review

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. © 2013 Copyright Taylor and Francis Group, LLC.
Original languageEnglish
Pages (from-to)1682-1700
JournalJournal of Applied Statistics
Volume40
Issue number8
DOIs
Publication statusPublished - 2013

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