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Fundamentals and Exchange Rate Prediction Revisited

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  • YI‐CHIUAN WANG
  • JYH‐LIN WU

Abstract

This paper measures latent fundamental exchange rates with independent component‐based rates constructed from a cross‐section of exchange rates and then uses their deviations from exchange rates to forecast. Empirical results indicate that the independent component‐based model and its Taylor rule and purchasing power parity augmented models are superior to the random walk in predicting exchange rates. These results are robust to several scenarios and are likely to be observed if the U.S. sources and the recursive scheme are applied. Our results reveal that information regarding the third moment of exchange rate changes is helpful to explain exchange rate movements.

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  • Yi‐Chiuan Wang & Jyh‐Lin Wu, 2015. "Fundamentals and Exchange Rate Prediction Revisited," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(8), pages 1651-1671, December.
    • Handle: RePEc:wly:jmoncb:v:47:y:2015:i:8:p:1651-1671
    DOI: 10.1111/jmcb.12287
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    Cited by:

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    2. Ponomareva, Natalia & Sheen, Jeffrey & Wang, Ben, 2015. "The Common Factor of Bilateral U.S. Exchange Rates: What is it Related to?," MPRA Paper 68966, University Library of Munich, Germany.
    3. Alexei Onatski & Chen Wang, 2021. "Spurious Factor Analysis," Econometrica, Econometric Society, vol. 89(2), pages 591-614, March.
    4. Kharrat, Sabrine & Hammami, Yacine & Fatnassi, Ibrahim, 2020. "On the cross-sectional relation between exchange rates and future fundamentals," Economic Modelling, Elsevier, vol. 89(C), pages 484-501.
    5. Esther Ruiz & Pilar Poncela, 2022. "Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components," Foundations and Trends(R) in Econometrics, now publishers, vol. 12(2), pages 121-231, November.
    6. Solat, Karo & Tsang, Kwok Ping, 2021. "Forecasting exchange rates with elliptically symmetric principal components," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1085-1091.

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