A Bayesian Markov-Switching Correlation Model for Contagion Analysis on Exchange Rate Markets
提出一种新的马尔可夫转换向量自回归模型，用两个独立马尔可夫链驱动相关性和波动率，用于分析汇率市场传染效应。应用于主要货币和亚太货币兑美元汇率，发现危机期间存在传染效应和相关性下降，且模型预测能力优于同类模型。
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