IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v8y2026i1p7-d1845225.html

Multi-Scale Explainable AI for RMB Exchange Rate Drivers

Author

Listed:
  • Jie Ji

    (State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
    School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China)

  • Shouyang Wang

    (State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
    School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
    Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China)

  • Yunjie Wei

    (State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
    School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
    Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China)

Abstract

To address the nonlinear nature of exchange rates where drivers vary by time horizon, this paper proposes a CEEMDAN-PE-CatBoost-SHAP framework. Analyzing USD/CNY data (2012–2024), we decomposed rates into high, medium, and low frequencies to bridge machine learning with economic interpretability. Empirical results revealed distinct frequency-dependent drivers: high-frequency fluctuations depend on market sentiment; medium-frequency variations follow Fed policies; and low-frequency trends reflect fundamentals like gold prices. SHAP analysis provides transparent attribution of these factors. This multi-scale approach isolates heterogeneous drivers, offering policymakers and investors a nuanced paradigm for managing currency risks. The study significantly clarifies how different economic factors shape exchange rate dynamics across varying time scales.

Suggested Citation

  • Jie Ji & Shouyang Wang & Yunjie Wei, 2026. "Multi-Scale Explainable AI for RMB Exchange Rate Drivers," Forecasting, MDPI, vol. 8(1), pages 1-20, January.
    • Handle: RePEc:gam:jforec:v:8:y:2026:i:1:p:7-:d:1845225
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/8/1/7/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/8/1/7/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:gam:jforec:v:8:y:2026:i:1:p:7-:d:1845225. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.