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A machine learning approach to forecasting carry trade returns

Author

Listed:
  • Xiao Wang
  • Xiao Xie
  • Yihua Chen
  • Borui Zhao

Abstract

Carry trade refers to a risky arbitrage in interest rate differentials between two currencies. Persistent excess carry trade returns pose a challenge to foreign exchange market efficiency. Using a data set of 10 currencies between 1990 and 2017, we find (i) a machine learning model, long short-term memory (LSTM) networks, forecast carry trade returns better than linear and threshold models and other machine learning models; and (ii) excess carry trade returns deteriorate after the 2007–2008 global financial crisis in all model forecasts, indicating that the uncovered interest rate parity may still hold in the long run.

Suggested Citation

  • Xiao Wang & Xiao Xie & Yihua Chen & Borui Zhao, 2022. "A machine learning approach to forecasting carry trade returns," Applied Economics Letters, Taylor & Francis Journals, vol. 29(13), pages 1199-1204, July.
    • Handle: RePEc:taf:apeclt:v:29:y:2022:i:13:p:1199-1204
    DOI: 10.1080/13504851.2021.1918624
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