G2TT
来源类型Discussion paper
规范类型论文
来源IDDP15305
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
URLhttps://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.
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