Multiple Chains Markov Switching Vector Autoregression

40 Pages Posted: 9 Sep 2020 Last revised: 15 Nov 2022

See all articles by Leopoldo Catania

Leopoldo Catania

Aarhus University - School of Business and Social Sciences; Aarhus University - CREATES

Date Written: November 10, 2022


The marginal distributions of US stocks and bond returns are characterized by well-defined Markovian regimes. Though, since there is very little coherence between these regimes, an highly parameterized system is usually estimated to properly describe their joint distribution. This paper proposes a new modelling framework for the bivariate MS-VAR model which better describes the US stocks and bond returns regimes. The proposed specification is composed by six latent Markov chains that drive the evolution of the parameters of the model. Results illustrate that the proposed specification provides more interpretable estimates of the conditional moments of the series and decoded latent variables with respect to the standard MS-VAR model. The maximum likelihood estimator is computed via an expectation conditional maximization algorithm with closed form conditional maximization steps. The large sample properties of the maximum likelihood estimator are established.

Keywords: Hidden Markov Models, Multiple Markov Chains, Expectation Conditional Maximization

JEL Classification: C32, C38, C51

Suggested Citation

Catania, Leopoldo, Multiple Chains Markov Switching Vector Autoregression (November 10, 2022). Available at SSRN: or

Leopoldo Catania (Contact Author)

Aarhus University - School of Business and Social Sciences ( email )

Fuglesangs Allé 4
Aarhus V, DK-8210
+4587165536 (Phone)


Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C

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