IDEAS home Printed from https://ideas.repec.org/a/kap/ecopln/v58y2025i4d10.1007_s10644-025-09895-y.html
   My bibliography  Save this article

Monetary policy stance and foreign currency lending: evidence from a persistently dollarized emerging market

Author

Listed:
  • Onder Ozgur

    (Ankara Yıldırım Beyazıt University)

  • Murat Aslan

    (Ankara Yıldırım Beyazıt University)

Abstract

This study explores how central bank policy changes influence Turkish banks’ foreign currency lending in one of the most dollarized emerging markets. Using quarterly data from 2002Q4 to 2024Q3 and applying a Bayesian Additive Regression Trees model within a machine learning framework, we uncover that higher policy rates amplify foreign currency lending—especially among larger banks—thereby weakening monetary transmission. The analysis also highlights the nonlinear and asymmetric effects of deposit dollarization, bank size, and macroeconomic conditions. These findings offer new insights into the structural constraints of monetary policy under persistent dollarization, with direct implications for policy design in similar economies.

Suggested Citation

  • Onder Ozgur & Murat Aslan, 2025. "Monetary policy stance and foreign currency lending: evidence from a persistently dollarized emerging market," Economic Change and Restructuring, Springer, vol. 58(4), pages 1-34, August.
    • Handle: RePEc:kap:ecopln:v:58:y:2025:i:4:d:10.1007_s10644-025-09895-y
    DOI: 10.1007/s10644-025-09895-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10644-025-09895-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10644-025-09895-y?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Machine learning; Dollarization; Türkiye; Monetary policy; Policy rate; Foreign currency lending;
    All these keywords.

    JEL classification:

    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    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:kap:ecopln:v:58:y:2025:i:4:d:10.1007_s10644-025-09895-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.