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Identifying Exchange Rate Common Factors

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  • Ryan Greenaway‐McGrevy
  • Nelson C. Mark
  • Donggyu Sul
  • Jyh‐Lin Wu

Abstract

Using recently developed model selection procedures, we determine that exchange rate returns are driven by a two‐factor model. We identify them as a dollar factor and a euro factor. Exchange rates are thus driven by global, U.S., and euro‐zone stochastic discount factors. The identified factors can also be given a risk‐based interpretation. Identification motivates multilateral models for bilateral exchange rates. Out‐of‐sample forecast accuracy of empirically identified multilateral models dominates the random walk and a bilateral purchasing power parity fundamentals prediction model. Twenty‐four‐month‐ahead forecast accuracy of the multilateral model dominates those of a principal components forecasting model.

Suggested Citation

  • Ryan Greenaway‐McGrevy & Nelson C. Mark & Donggyu Sul & Jyh‐Lin Wu, 2018. "Identifying Exchange Rate Common Factors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(4), pages 2193-2218, November.
  • Handle: RePEc:wly:iecrev:v:59:y:2018:i:4:p:2193-2218
    DOI: 10.1111/iere.12334
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    References listed on IDEAS

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    1. Tarek A Hassan & Rui C Mano, 2019. "Forward and Spot Exchange Rates in a Multi-Currency World," The Quarterly Journal of Economics, Oxford University Press, vol. 134(1), pages 397-450.
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    4. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    5. Mark, Nelson C. & Sul, Donggyu, 2001. "Nominal exchange rates and monetary fundamentals: Evidence from a small post-Bretton woods panel," Journal of International Economics, Elsevier, vol. 53(1), pages 29-52, February.
    6. Cheung, Yin-Wong & Chinn, Menzie D. & Pascual, Antonio Garcia, 2005. "Empirical exchange rate models of the nineties: Are any fit to survive?," Journal of International Money and Finance, Elsevier, vol. 24(7), pages 1150-1175, November.
    7. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "A panel project on purchasing power parity: Mean reversion within and between countries," Journal of International Economics, Elsevier, vol. 40(1-2), pages 209-224, February.
    8. Jason Parker & Donggyu Sul, 2016. "Identification of Unknown Common Factors: Leaders and Followers," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 227-239, April.
    9. Groen, Jan J J, 2005. "Exchange Rate Predictability and Monetary Fundamentals in a Small Multi-country Panel," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 495-516, June.
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    11. Berg, Kimberly A. & Mark, Nelson C., 2015. "Third-country effects on the exchange rate," Journal of International Economics, Elsevier, vol. 96(2), pages 227-243.
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    Cited by:

    1. Burdon, D. & Potts, T. & McKinley, E. & Lew, S. & Shilland, R. & Gormley, K. & Thomson, S. & Forster, R., 2019. "Expanding the role of participatory mapping to assess ecosystem service provision in local coastal environments," Ecosystem Services, Elsevier, vol. 39(C).
    2. HORIE, Tetsushi & YAMAMOTO, Yohei, 2016. "Testing for Speculative Bubbles in Large-Dimensional Financial Panel Data Sets," Discussion Papers 2016-04, Graduate School of Economics, Hitotsubashi University.
    3. Shuo Cao & Hongyi Chen, 2017. "Exchange Rate Movements and Fundamentals: Impact of Oil Prices and China¡¯s Growth," Working Papers 042017, Hong Kong Institute for Monetary Research.
    4. Pierre Guerin & Danilo Leiva-Leon & Massimiliano Marcellino, 2016. "Markov-Switching Three-Pass Regression Filter," Working Papers 591, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    5. Aloosh, Arash & Bekaert, Geert, 2019. "Currency Factors," CEPR Discussion Papers 13464, C.E.P.R. Discussion Papers.
    6. George Kapetanios & M. Hashem Pesaran & Simon Reese, 2018. "A Residual-based Threshold Method for Detection of Units that are Too Big to Fail in Large Factor Models," CESifo Working Paper Series 7401, CESifo Group Munich.

    More about this item

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

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