A new Bayesian model for contagion and interdependence
开发了一个带Leamer修正的灵活贝叶斯时变参数模型，用于测量传染和相互依赖，并应用于智利外汇市场和英国新冠疫情，发现阿根廷危机未传染至智利，英国政府未能有效阻止第二波疫情输入。
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.