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Why is it so difficult to outperform the random walk in exchange rate forecasting?

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  • Imad Moosa

Abstract

A simulation exercise is used to demonstrate the difficulty to outperform the random walk in exchange rate forecasting if forecasting accuracy is judged by the Root Mean Square Error (RMSE) or similar criteria that depend on the magnitude of the forecasting error. It is shown that, as the exchange rate volatility rises, the RMSE of the model rises faster than that of the random walk. While the literature considers this finding to be a puzzle that casts a big shadow of doubt on the soundness of international monetary economics, the results show that failure to outperform the random walk, in both in-sample and out-of-sample forecasting, should be the rule rather than the exception. However, the results do not imply that the random walk is unbeatable, because it can be easily outperformed if forecasting accuracy is judged according to criteria such as direction accuracy and profitability.

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  • Imad Moosa, 2013. "Why is it so difficult to outperform the random walk in exchange rate forecasting?," Applied Economics, Taylor & Francis Journals, vol. 45(23), pages 3340-3346, August.
    • Handle: RePEc:taf:applec:v:45:y:2013:i:23:p:3340-3346
    DOI: 10.1080/00036846.2012.709605
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    1. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
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    Cited by:

    1. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
    2. Kelly Burns, 2016. "A Reconsideration of the Meese-Rogoff Puzzle: An Alternative Approach to Model Estimation and Forecast Evaluation," Multinational Finance Journal, Multinational Finance Journal, vol. 20(1), pages 41-83, March.
    3. Salisu, Afees A. & Ndako, Umar B., 2018. "Modelling stock price–exchange rate nexus in OECD countries: A new perspective," Economic Modelling, Elsevier, vol. 74(C), pages 105-123.
    4. Imad Moosa & Kelly Burns, 2013. "The monetary model of exchange rates is better than the random walk in out-of-sample forecasting," Applied Economics Letters, Taylor & Francis Journals, vol. 20(14), pages 1293-1297, September.
    5. Afees A. Salisu & Juncal Cuñado & Kazeem Isah & Rangan Gupta, 2021. "Stock markets and exchange rate behavior of the BRICS," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1581-1595, December.
    6. Salisu, Afees A. & Olaniran, Abeeb & Tchankam, Jean Paul, 2022. "Oil tail risk and the tail risk of the US Dollar exchange rates," Energy Economics, Elsevier, vol. 109(C).
    7. Peter Golit & Afees Salisu & Akinwunmi Akintola & Faustina Nsonwu & Itoro Umoren, 2019. "Exchange Rate And Interest Rate Differential In G7 Economies," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 22(3), pages 263-286, October.
    8. Salisu, Afees A. & Ogbonna, Ahamuefula E., 2019. "Another look at the energy-growth nexus: New insights from MIDAS regressions," Energy, Elsevier, vol. 174(C), pages 69-84.
    9. Imad A. Moosa, 2015. "The random walk versus unbiased efficiency: can we separate the wheat from the chaff?," Journal of Post Keynesian Economics, Taylor & Francis Journals, vol. 38(2), pages 251-279, October.
    10. Afees A. Salisu & Juncal Cunado & Kazeem Isah & Rangan Gupta, 2020. "Oil Price and Exchange Rate Behaviour of the BRICS for Over a Century," Working Papers 202064, University of Pretoria, Department of Economics.
    11. Tasadduq Imam, 2021. "Model selection for one‐day‐ahead AUD/USD, AUD/EUR forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1808-1824, April.
    12. Moosa, Imad & Burns, Kelly, 2014. "The unbeatable random walk in exchange rate forecasting: Reality or myth?," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 69-81.
    13. Burns, Kelly & Moosa, Imad A., 2015. "Enhancing the forecasting power of exchange rate models by introducing nonlinearity: Does it work?," Economic Modelling, Elsevier, vol. 50(C), pages 27-39.
    14. Afees A. Salisu & Abdulsalam Abidemi Sikiru, 2021. "Palm Oil Price–Exchange Rate Nexus In Indonesia And Malaysia," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 24(2), pages 169-180, June.
    15. Moosa, Imad A. & Vaz, John, 2018. "Direct and Indirect Forecasting of Cross Exchange Rates," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 71(2), pages 173-190.
    16. Enzo Cassino & David Oxley, 2013. "How Does the Exchange Rate Affect the Real Economy? A Literature Survey," Treasury Working Paper Series 13/26, New Zealand Treasury.

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