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

Abstract

AbstractIn 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-