| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP15305 |
| DP15305 Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio | |
| Ilias Filippou; David Rapach; Mark Taylor; Guofu Zhou | |
| 发表日期 | 2020-09-17 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfitting, we use the elastic net to estimate a high-dimensional panel predictive regression and find that the resulting forecast consistently outperforms the naive no-change benchmark, which has proven difficult to beat in the literature. The forecast also markedly improves the performance of a carry trade portfolio, especially during and after the global financial crisis. When we allow for more complex deep learning models, nonlinearities do not appear substantial in the data. |
| 主题 | International Macroeconomics and Finance |
| 关键词 | Exchange rate predictability Elastic net Carry trade Deep neural network |
| URL | https://cepr.org/publications/dp15305 |
| 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/544282 |
| 推荐引用方式 GB/T 7714 | Ilias Filippou,David Rapach,Mark Taylor,et al. DP15305 Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio. 2020. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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