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• W2980470616 abstract "We establish the out-of-sample predictability of monthly exchange rates via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To better guard against overfitting in our high-dimensional and noisy data environment, we make additional adjustments to “off-the-shelf” implementations of machine learning techniques, including imposing economic constraints. The resulting forecasts consistently outperform the no-change benchmark, which has proven difficult to beat. Country characteristics are important for forecasting, once they interact with global variables. Machine learning forecasts also markedly improve the performance of a carry trade portfolio, especially since the global financial crisis." @default.
• W2980470616 created "2019-10-25" @default.
• W2980470616 creator A5005644819 @default.
• W2980470616 creator A5012239666 @default.
• W2980470616 creator A5020392888 @default.
• W2980470616 creator A5080266703 @default.
• W2980470616 date "2019-01-01" @default.
• W2980470616 modified "2023-10-16" @default.
• W2980470616 title "The Rise and Fall of the Carry Trade: Links to Exchange Rate Predictability" @default.
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• W2980470616 doi "https://doi.org/10.2139/ssrn.3455713" @default.
• W2980470616 hasPublicationYear "2019" @default.
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