This study evaluates the credit risk of sustainable loans in a preferential capital requirement programme. We utilise loanlevel data from a uniquely implemented programme from Hungary, applying logistic regressions and survival analysis techniques. We observe a significantly reduced credit risk for firms with renewable energy and electromobility loans, even after accounting for all relevant covariates. Models incorporating green characteristics predict a substantially lower credit risk for firms with green loans compared to models excluding green characteristics. These results are economically significant and robust to model specifications, alternative definitions of green firms and varying default definitions. We show that green loans' lower probability of default can justify a reduction of several percentage points in capital requirements
Balint Vargedo (),
Csaba Burger () and
Donat Kim ()
Additional contact information
Balint Vargedo: Magyar Nemzeti Bank (Central Bank of Hungary)
Csaba Burger: Magyar Nemzeti Bank (Central Bank of Hungary)
Donat Kim: Magyar Nemzeti Bank (Central Bank of Hungary)
No 2025/2, MNB Working Papers from Magyar Nemzeti Bank (Central Bank of Hungary)
Keywords: sustainable finance; financial stability; capital requirement; green finance; default probability; green transition; central bank mandates. (search for similar items in EconPapers)
JEL-codes: E58 G21 G33 O16 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.mnb.hu/en/publications/studies-publica ... e-in-emerging-europe (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:mnb:wpaper:2025/2
Access Statistics for this paper
More papers in MNB Working Papers from Magyar Nemzeti Bank (Central Bank of Hungary) Contact information at EDIRC.
Bibliographic data for series maintained by Lorant Kaszab ( this e-mail address is bad, please contact ).