Nėra lietuvių kalba
Sophia Chen
- 20 April 2020
- WORKING PAPER SERIES - No. 2395Details
- Abstract
- This paper presents a new dataset on the dynamics of non-performing loans (NPLs) during 88 banking crises since 1990. The data show similarities across crises during NPL build-ups but less so during NPL resolutions. We find a close relationship between NPL problems—elevated and unresolved NPLs—and the severity of post-crisis recessions. A machine learning approach identifies a set of pre-crisis predictors of NPL problems related to weak macroeconomic, institutional, corporate, and banking sector conditions. Our findings suggest that reducing pre-crisis vulnerabilities and promptly addressing NPL problems during a crisis are important for post-crisis output recovery.
- JEL Code
- E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
N10 : Economic History→Macroeconomics and Monetary Economics, Industrial Structure, Growth, Fluctuations→General, International, or Comparative
N20 : Economic History→Financial Markets and Institutions→General, International, or Comparative
Annexes- 20 April 2020
- ANNEX