Improving Markov switching models using realized variance

Liu, Jia and Maheu, John M (2015): Improving Markov switching models using realized variance.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: infinite hidden Markov model, realized covariance, density forecast, MCMC
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; G - Financial Economics > G1 - General Financial Markets

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