A Bayesian Markov-Switching Correlation Model for Contagion Analysis on Exchange Rate Markets. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Casarin, Roberto; Sartore, Domenico; Tronzano, Marco
• Is Part Of: Journal of business & economic statistics. Volume 36:Issue 1(2018)
• Page Start: 101
• Page End: 114
• Publication Date: 2018-01-02
• Subjects: Bayesian VAR -- Contagion -- Exchange rates -- Markov-switching -- Multivariate stochastic volatility -- Stochastic correlation; Economics -- Statistical methods -- Periodicals; Commercial statistics -- Periodicals; Économie politique -- Méthodes statistiques -- Périodiques; Statistique commerciale -- Périodiques; 330.015195
• DOI: 10.1080/07350015.2015.1137757
• Languages: English
• ISSNs: 0735-0015

Abstract

Abstract : This article develops a new Markov-switching vector autoregressive (VAR) model with stochastic correlation for contagion analysis on financial markets. The correlation and the log-volatility dynamics are driven by two independent Markov chains, thus allowing for different effects such as volatility spill-overs and correlation shifts with various degrees of intensity. We outline a suitable Bayesian inference procedure based on Markov chain Monte Carlo algorithms. We then apply the model to some major and Asian-Pacific cross rates against the U.S. dollar and find strong evidence supporting the existence of contagion effects and correlation drops during crises, closely in line with the stylized facts outlined in the contagion literature. A comparison of this model with its closest competitors, such as a time-varying parameter VAR, reveals that our model has a better predictive ability. Supplementary materials for this article are available online