Macroeconomic and Financial Modelling in an Era of Extremes

该项目旨在开发方法，使经济和金融领域的主流模型能够更好地反映尾部事件——低概率极端事件，如全球金融危机和2019冠状病毒病大流行。它打算解决这些模型估计中的基本技术挑战，为这些模型的反事实分析开发一个连贯的框架，并提出在大数据环境中应用这些模型的方法。预期成果包括对全球经济和金融体系尾部风险传导的新见解。这将带来巨大的好处，包括为负责在极端事件面前维持稳定的澳大利亚和国际政策制定者提供指导。

This project aims to develop methods to allow workhorse models in economics and finance to better reflect tail events--low probability extreme events, such as the Global Financial Crisis and the COVID-19 pandemic. It intends to address fundamental technical challenges in the estimation of such models, develop a coherent framework for counterfactual analysis of these models and propose methods to apply these models in a big-data environment. Expected outcomes include new insights into the transmission of tail risks in the global economic and financial system. This should provide significant benefits, including guidance to Australian and international policymakers charged with maintaining stability in the face of extreme events.