| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP15618 |
| DP15618 Answering the Queen: Machine Learning and Financial Crises | |
| Jeremy FOULIARD; Michael Howell; Helene Rey | |
| 发表日期 | 2022-01-23 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | Financial crises cause economic, social and political havoc. Macroprudential policies are gaining traction but are still severely under-researched compared to monetary and fiscal policy. We use the general framework of sequential predictions, also called online machine learning, to forecast crises out-of-sample. Our methodology is based on model aggregation and is “meta-statistical”, since we can incorporate any predictive model of crises in our analysis and test its ability to add information, without making any assumption on the data generating process. We predict systemic financial crises twelve quarters ahead out-of-sample with high signal-to-noise ratio. Our approach guarantees that picking certain time dependent sets of weights will be asymptotically similar for out-of-sample forecasts to the best ex post combination of models; it also guarantees that we outperform any individual forecasting model asymptotically. We analyse which models provide the most information for our predictions at each point in time and for each country, providing some insights into economic mechanisms underlying the buildup of risk in economies. |
| 主题 | Financial Economics ; International Macroeconomics and Finance |
| URL | https://cepr.org/publications/dp15618-0 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/545891 |
| 推荐引用方式 GB/T 7714 | Jeremy FOULIARD,Michael Howell,Helene Rey. DP15618 Answering the Queen: Machine Learning and Financial Crises. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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