Leopoldo Catania

TY - UNPB

T1 - Multiple Chains Markov Switching Vector Autoregression

AU - Catania, Leopoldo

PY - 2020

Y1 - 2020

N2 - We present a new modelling framework for the bivariate hidden Markov model. The proposed specification is composed by five latent Markovian chains which drive the evolution of the parameters of a bivariate Gaussian distribution. The maximum likelihood estimator is computed via an expectation conditional maximization algorithm with closed form conditional maximization steps, specifically developed for our model. Identification of model parameters, as well as consistency and asymptotic Normality of the maximum likelihood estimator are discussed. Finite sample properties of the estimator are investigated in an extensive simulation study. An empirical application with the bivariate series of US stocks and bond returns illustrates the benefits of the new specification with respect to the standard hidden Markov model.

AB - We present a new modelling framework for the bivariate hidden Markov model. The proposed specification is composed by five latent Markovian chains which drive the evolution of the parameters of a bivariate Gaussian distribution. The maximum likelihood estimator is computed via an expectation conditional maximization algorithm with closed form conditional maximization steps, specifically developed for our model. Identification of model parameters, as well as consistency and asymptotic Normality of the maximum likelihood estimator are discussed. Finite sample properties of the estimator are investigated in an extensive simulation study. An empirical application with the bivariate series of US stocks and bond returns illustrates the benefits of the new specification with respect to the standard hidden Markov model.

M3 - Working paper

BT - Multiple Chains Markov Switching Vector Autoregression

ER -