A Markov-switching regression model with non-Gaussian innovations: estimation and testing

Luca De Angelis and Viroli Cinzia
Additional contact information
Viroli Cinzia: University of Bologna, Department of Statistical Sciences, via Belle Arti, 41, Bologna 40126, Italy

Studies in Nonlinear Dynamics & Econometrics, 2017, vol. 21, issue 2, 22

Abstract: In this paper we propose a very general multivariate Markov-switching regression (MSR) model considering the normal inverse Gaussian (NIG) distribution as conditional form of financial returns and model innovations. It is indeed well-known that the Gaussian distribution is not able to capture many stylized facts of the return series such as skewness, excess kurtosis and heavy tails. Through a large simulation study and an empirical analysis of the US stock market, we show that a NIG-based MSR model allows to adequately account for both skewness and fat tails in the data and, according to model selection criteria, is the best overall model in the majority of the cases considered, even preferred over other popular distributional assumptions such as Student-t and GED. We develop an EM algorithm which allows the estimation of the model parameters in closed form. As a natural byproduct of the algorithm we also derive the scores of the model estimators that allow us to perform dynamic specification tests to check for autocorrelation and for the violation of the first-order Markov assumption.

Keywords: market regimes; Markov-switching; normal inverse Gaussian distribution; return time series analysis (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/snde-2015-0118 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sndecm:v:21:y:2017:i:2:p:22:n:3

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/snde/html

DOI: 10.1515/snde-2015-0118

Access Statistics for this article

Studies in Nonlinear Dynamics & Econometrics is currently edited by Bruce Mizrach

More articles in Studies in Nonlinear Dynamics & Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().