We develop a flexible Bayesian time-varying parameter model with a Leamer correction to measure contagion and interdependence. Our proposed framework facilitates a model-based identification mechanism for static and dynamic interdependence. We also allow for fat-tails stochastic volatility within the model, which enables us to capture volatility clustering and outliers in high-frequency financial data. We apply our new proposed framework to two empirical applications: the Chilean foreign exchange market during the Argentine crisis of 2001 and the recent Covid-19 pandemic in the United Kingdom. We find no evidence of contagion effects from Argentina or Brazil to Chile and three additional key insights compared to Ciccarelli and Rebucci 2006 study. For the Covid-19 pandemic application, our results convey that the United Kingdom government was largely ineffective in preventing the importation of Covid-19 cases from European countries during the second wave of the pandemic.
- Bayesian estimation
- omitted variable bias
- stochastic volatility
- time-varying parameter models