IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v43y2024i3p644-660.html
   My bibliography  Save this article

RMB exchange rate forecasting using machine learning methods: Can multimodel select powerful predictors?

Author

Listed:
  • Xing Yu
  • Yanyan Li
  • Xinxin Wang

Abstract

This paper aims to study the phased influencing factors of renminbi (RMB) exchange rate (CNY against USD) and investigate the predictability of the factors selected by multimodel. We first take the time points when China's main exchange reform policies are launched as the demarcation points and divide the entire sample from July 2005 to December 2020 into three periods. Then, we select the potential predictors using several sources, including all factors (without any selection), the factors selected by each of the five commonly used machine learning methods, the significantly correlated factors selected by traditional regression analysis method, and multimodel‐driven factors. Finally, we predict the exchange rate based on the above selected factors and compare the prediction results. The research results show that the main influencing factors are different in different periods, and the influence of phase events cannot be ignored. Even if their influence on the exchange rate has decreased as a result of the “811” exchange rate reform, the money supply and foreign exchange reserves continue to be the primary drivers of RMB exchange rates during the whole period of the sample. Additionally, RMB exchange rate forward is a robust influencing factor in all periods. By comparing the forecast errors, we find that the prediction accuracy of the factors selected based on multimodel is higher than that of the factors selected based on a single method or the tradition method. The findings of this paper provide the following insights for exchange rate managers: In exchange rate risk management, it is important to pay attention to the impact of macroeconomic factors such as foreign exchange reserves and the impact of staged events, and market expectations of exchange rates are equally important. At the technical level, it is recommended to improve the forecasting accuracy by forecasting exchange rates based on common factors selected by multiple better machine learning methods simultaneously rather than those selected by a single method.

Suggested Citation

  • Xing Yu & Yanyan Li & Xinxin Wang, 2024. "RMB exchange rate forecasting using machine learning methods: Can multimodel select powerful predictors?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 644-660, April.
    • Handle: RePEc:wly:jforec:v:43:y:2024:i:3:p:644-660
    DOI: 10.1002/for.3054
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.3054
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.3054?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Li, Xue & Liu, Yanghui & Li, Hanxu & Li, Jie, 2021. "Onshore spot and offshore forward markets for RMB: Evidence from the “8.11” exchange rate regime reform," China Economic Review, Elsevier, vol. 67(C).
    2. McCauley, Robert N. & Shu, Chang, 2019. "Recent renminbi policy and currency co-movements," Journal of International Money and Finance, Elsevier, vol. 95(C), pages 444-456.
    3. Daniel J. Henderson & Chris Papageorgiou & Christopher F. Parmeter, 2012. "Growth Empirics without Parameters," Economic Journal, Royal Economic Society, vol. 122(559), pages 125-154, March.
    4. Pasquale Della Corte & Steven J. Riddiough & Lucio Sarno, 2016. "Currency Premia and Global Imbalances," The Review of Financial Studies, Society for Financial Studies, vol. 29(8), pages 2161-2193.
    5. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    6. Tamal Datta Chaudhuri & Indranil Ghosh, 2016. "Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework," Papers 1607.02093, arXiv.org.
    7. Chiappini, Raphaël & Lahet, Delphine, 2020. "Exchange rate movements in emerging economies - Global vs regional factors in Asia," China Economic Review, Elsevier, vol. 60(C).
    8. Gyntelberg, Jacob & Loretan, Mico & Subhanij, Tientip, 2018. "Private information, capital flows, and exchange rates," Journal of International Money and Finance, Elsevier, vol. 81(C), pages 40-55.
    9. Sebastian Heiden & Christian Klein & Bernhard Zwergel, 2013. "Beyond Fundamentals: Investor Sentiment and Exchange Rate Forecasting," European Financial Management, European Financial Management Association, vol. 19(3), pages 558-578, June.
    10. Mr. Luca A Ricci & Mr. Jonathan David Ostry & Mr. Jaewoo Lee & Mr. Alessandro Prati & Mr. Gian M Milesi-Ferretti, 2008. "Exchange Rate Assessments: CGER Methodologies," IMF Occasional Papers 2008/002, International Monetary Fund.
    11. Kasman, Adnan & Ayhan, Duygu, 2008. "Foreign exchange reserves and exchange rates in Turkey: Structural breaks, unit roots and cointegration," Economic Modelling, Elsevier, vol. 25(1), pages 83-92, January.
    12. Xu, Zhenhui, 2003. "Purchasing power parity, price indices, and exchange rate forcasts," Journal of International Money and Finance, Elsevier, vol. 22(1), pages 105-130, February.
    13. Cermeño, Rodolfo & Grier, Robin & Grier, Kevin, 2010. "Elections, exchange rates and reform in Latin America," Journal of Development Economics, Elsevier, vol. 92(2), pages 166-174, July.
    14. Melvin, Michael & Menkhoff, Lukas & Schmeling, Maik, 2009. "Exchange rate management in emerging markets: Intervention via an electronic limit order book," Journal of International Economics, Elsevier, vol. 79(1), pages 54-63, September.
    15. Pasquale Della Corte & Steven J. Riddiough & Lucio Sarno, 2016. "Currency Premia and Global Imbalances," Review of Financial Studies, Society for Financial Studies, vol. 29(8), pages 2161-2193.
    16. Jaebeom Kim, 2006. "Reconsidering Real Interest Parity for Traded and Nontraded Goods," Review of International Economics, Wiley Blackwell, vol. 14(2), pages 306-315, May.
    17. Liu, Clark & Wang, Ben Zhe & Wang, Huanhuan & Zhang, Ji, 2019. "What drives fluctuations in exchange rate growth in emerging markets – A multi-level dynamic factor approach," Economic Systems, Elsevier, vol. 43(2), pages 1-1.
    18. Pontines, Victor & Rajan, Ramkishen S., 2011. "Foreign exchange market intervention and reserve accumulation in emerging Asia: Is there evidence of fear of appreciation?," Economics Letters, Elsevier, vol. 111(3), pages 252-255, June.
    19. Chen, Wei & Xu, Huilin & Jia, Lifen & Gao, Ying, 2021. "Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants," International Journal of Forecasting, Elsevier, vol. 37(1), pages 28-43.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Keddad, Benjamin & Sato, Kiyotaka, 2022. "The influence of the renminbi and its macroeconomic determinants: A new Chinese monetary order in Asia?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    2. Rubaszek, Michał & Beckmann, Joscha & Ca' Zorzi, Michele & Kwas, Marek, 2022. "Boosting carry with equilibrium exchange rate estimates," Working Paper Series 2731, European Central Bank.
    3. Ca’ Zorzi, Michele & Rubaszek, Michał, 2023. "How many fundamentals should we include in the behavioral equilibrium exchange rate model?," Economic Modelling, Elsevier, vol. 118(C).
    4. Stijn Claessens & M Ayhan Kose, 2017. "Asset prices and macroeconomic outcomes: a survey," BIS Working Papers 676, Bank for International Settlements.
    5. Stijn Claessens & M Ayhan Kose, 2018. "Frontiers of macrofinancial linkages," BIS Papers, Bank for International Settlements, number 95.
    6. Yin, Libo, 2020. "Can the intermediary capital risk predict foreign exchange rates?," Finance Research Letters, Elsevier, vol. 37(C).
    7. Yang Liu & Mariano Croce & Ivan Shaliastovich & Ric Colacito, 2016. "Volatility Risk Pass-Through," 2016 Meeting Papers 135, Society for Economic Dynamics.
    8. Özmen, M. Utku & Yılmaz, Erdal, 2017. "Co-movement of exchange rates with interest rate differential, risk premium and FED policy in “fragile economies”," Emerging Markets Review, Elsevier, vol. 33(C), pages 173-188.
    9. Berg, Kimberly A. & Mark, Nelson C., 2018. "Measures of global uncertainty and carry-trade excess returns," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 212-227.
    10. Hassan, Ramin & Loualiche, Erik & Pecora, Alexandre R. & Ward, Colin, 2023. "International trade and the risk in bilateral exchange rates," Journal of Financial Economics, Elsevier, vol. 150(2).
    11. Fousseni Chabi-Yo & Riccardo Colacito, 2019. "The Term Structures of Coentropy in International Financial Markets," Management Science, INFORMS, vol. 65(8), pages 3541-3558, August.
    12. Ferdinand Dreher & Johannes Gräb & Thomas Kostka, 2020. "From carry trades to curvy trades," The World Economy, Wiley Blackwell, vol. 43(3), pages 758-780, March.
    13. Ali Trabelsi Karoui & Aida Kammoun, 2021. "Exchange Rate Determination: Mixed Microstructural and Macroeconomic Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 11(3), pages 89-106.
    14. Choi, Jin Ho & Suh, Sangwon, 2021. "A filtered currency carry trade," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    15. Fan, Zhenzhen & Londono, Juan M. & Xiao, Xiao, 2022. "Equity tail risk and currency risk premiums," Journal of Financial Economics, Elsevier, vol. 143(1), pages 484-503.
    16. Berg, Kimberly A. & Mark, Nelson C., 2018. "Global macro risks in currency excess returns," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 300-315.
    17. Bathia, Deven & Demirer, Riza & Gupta, Rangan & Kotzé, Kevin, 2021. "Unemployment fluctuations and currency returns in the United Kingdom: Evidence from over one and a half century of data," Journal of Multinational Financial Management, Elsevier, vol. 61(C).
    18. Nucera, Federico, 2017. "Unemployment fluctuations and the predictability of currency returns," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 88-106.
    19. Zaremba, Adam & Czapkiewicz, Anna, 2017. "The cross section of international government bond returns," Economic Modelling, Elsevier, vol. 66(C), pages 171-183.
    20. Chen, Chih-Nan & Lin, Chien-Hsiu, 2020. "The sources of pricing factors underlying the cross-section of currency returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 250-265.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:43:y:2024:i:3:p:644-660. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.