Improving Markov switching models using realized variance

Jia Liu and John Maheu

MPRA Paper from University Library of Munich, Germany

Abstract: This paper proposes a class of models that jointly model returns and ex-post variance measures under a Markov switching framework. Both univariate and multivariate return versions of the model are introduced. Bayesian estimation can be conducted under a fixed dimension state space or an infinite one. The proposed models can be seen as nonlinear common factor models subject to Markov switching and are able to exploit the information content in both returns and ex-post volatility measures. Applications to U.S. equity returns and foreign exchange rates compare the proposed models to existing alternatives. The empirical results show that the joint models improve density forecasts for returns and point predictions of return variance. The joint Markov switching models can increase the precision of parameter estimates and sharpen the inference of the latent state variable.

Keywords: infinite hidden Markov model; realized covariance; density forecast; MCMC (search for similar items in EconPapers)
JEL-codes: C11 C32 C51 C58 G1 (search for similar items in EconPapers)
Date: 2015-09-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-pr~, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:71120

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