Exchange rate predictability: fact or fiction?

Karen Jackson*, Georgios Magkonis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The present study investigates the factors that affect the forecasting performance of several models that have been used for exchange rate prediction. We provide a quantitative survey collecting 8,413 reported forecast errors and we investigate which forecasting characteristics tend to improve forecasting ability. According to our evidence, predictions can beat random walk when certain types of models and econometric methods are used. In particular, linear specifications based on PPP outperform random walk. Furthermore, higher data frequency and longer forecasting horizon also improve forecasting performance. In this way, we identify under which conditions it is feasible to solve the `Meese-Rogoff' puzzle.
Original languageEnglish
Article number103026
Number of pages12
JournalJournal of International Money and Finance
Early online date19 Feb 2024
Publication statusEarly online - 19 Feb 2024


  • Exchange rates
  • forecasting performance
  • meta-analysis

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