Multivariate Markov Switching Model Research Articles

The multi-chain Markov switching model

In many real phenomena the behaviour of a certain variable, subject to different regimes, depends on the state of other variables or the same variable observed in other subjects, so the knowledge of the state of the latter could be important to forecast the state of the former. In this paper a particular multivariate Markov switching model is developed to represent this case. The transition probabilities of this model are characterized by the dependence on the regime of the other variables. The estimation of the transition probabilities provides useful information for the researcher to forecast the regime of the variables analysed. Theoretical background and an application are shown. Copyright © 2005 John Wiley & Sons, Ltd.

How well do Markov switching models describe actual business cycles? The case of synchronization

AbstractThe objective of this paper is to evaluate the effectiveness of using a Markov switching model to measure the synchronization of business cycles. We use a Bayesian, Gibbs sampling approach to estimate a multivariate Markov switching model of GDP growth for several countries. We look for evidence of synchronization across countries in the sense of common Markov states, covariance of impulses and a long‐run co‐integrating relationship. We then use the fitted data implied by the posterior distribution of the Markov switching VAR, in conjunction with a dating rule, to obtain the posterior distribution of binary business cycle states. We use these to investigate the posterior distributions of non‐parametric measures of synchronization described by Harding and Pagan (2003) and compare them with similar measures obtained from standard reference chronologies. As a point of reference, we repeat this exercise using simulated data from a linear VAR.We find no evidence of a common Markov state, but some evidence of the propagation of country‐specific disturbances across countries and of a co‐integrating relationship between the United States and Canada. Posterior odds ratios overwhelmingly favour the Markov switching model over the linear VAR and we find that the posterior distributions of the non‐parametric measures of synchronization produced by the Markov switching VAR match the data more closely than those produced by the linear VAR. Copyright © 2005 John Wiley & Sons, Ltd.

CREDIBILITY, MACROECONOMIC FUNDAMENTALS AND MARKOV REGIME SWITCHES IN THE EMS

AbstractThe main objective of this paper is to use the Markov regime‐switching modelling framework to describe and analyse the credibility of a number of countries participating in the European Monetary System during 1980–1998. Our credibility indicator, based on Hughes Hallet et al.'s (1997) methodology, is subject to discrete regime shifts and is made dependent on macroeconomic fundamentals. We carry out extensive testing to assess the specification of the Markov regime‐switching model and the potential existence of permanent breaks. A contribution of our paper is the specification of a multivariate Markov switching model that allows us to examine whether macroeconomic variables have asymmetric effects on credibility. Another contribution is the specification of a regime‐switching model with time‐varying transition probabilities, which enables us to determine whether changes in macroeconomic variables can trigger switches between the low and high credibility regimes. We find strong evidence of regime switching behaviour in all countries. Both the level of credibility and the transition probabilities display an asymmetric response to changes in macroeconomic variables, with the stance of fiscal policy exerting the most systematic influence in all countries.

A three-regime real-time indicator for the US economy

This note proposes a monthly real-time cyclical indicator for the US economy computed through a three-regime multivariate Markov-Switching model. The model specification is based on the reconciliation of the concepts of growth and business cycles.

Analysis of the US business cycle with a Vector-Markov-switching model

This paper identifies turning points for the US ‘business cycle’ using information from different time series. The model, a multivariate Markov-switching model, assumes that each series is characterized by a mixture of two normal distributions (a high and low mean) with the switching from one to the other determined by a common Markov process. The procedure is applied to the series composing the composite coincident indicator in the USA to obtain business cycle turning points. The business cycle chronology is closer to the NBER reference cycle than the turning points obtained from the individual series using a univariate model. The model is also used to forecast the series with some encouraging results. Copyright © 2001 John Wiley & Sons, Ltd.

Does Correlation between Stock Returns Really Increase During Turbulent Period?

Correlations between international equity markets are often claimed to increase during periods of high volatility, therefore the benefits of international diversification are reduced when they are most needed, i.e. during crises. In this paper, we investigate the relationship between international correlation and stock-market turbulence. We estimate a multivariate Markov-switching model, in which the correlation matrix is allowed to vary across regimes. Subsequently, we test the null hypothesis that correlations are regime independent. Using weekly stock returns for the S&P, the DAX and the FTSE over the period 1988-1999, we find that international correlations significantly increased during turbulent periods.