A dynamic analysis of stock markets using a hidden Markov model
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-<italic>t</italic> innovations both in-sample and out-of-sample, giving financial operators some appealing investment strategies.
Year of publication: |
2013
|
---|---|
Authors: | Angelis, Luca De ; Paas, Leonard J. |
Published in: |
Journal of Applied Statistics. - Taylor & Francis Journals, ISSN 0266-4763. - Vol. 40.2013, 8, p. 1682-1700
|
Publisher: |
Taylor & Francis Journals |
Saved in:
Saved in favorites
Similar items by person
-
The Dynamic Analysis and Prediction of Stock Markets through the Latent Markov Model
De Angelis, Luca, (2009)
-
The dynamic analysis and prediction of stock markets through the latent Markov model
De Angelis, Luca, (2010)
-
When too much is too little : evaluating the Italian Startup Act
De Angelis, Luca, (2018)
- More ...