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The Dynamics of Non-Performing Loans during Banking Crises: A New Database, Anil Ari, Sophia Chen, Lev Ratnovski

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
Table Of Contents
Cover -- Contents -- I. Introduction -- II. A Dataset on NPL Dynamics During Banking Crises -- a. Data construction -- b. Stylized facts -- III. Post-Crisis NPLs and Output -- a. Specification -- b. Results -- IV. Predictors of NPL Dynamics -- a. Methodology -- b. Candidate NPL predictors -- c. Results -- V. Application: NPLs in the European Crisis Countries -- VI. Conclusion -- References -- Tables -- Table 1. Summary statistics -- Table 2. NPLs and output dynamics -- Table 3. Model selection results -- Table 4. Summary of predictors -- Figures -- Figure 1. A typical NPL trajectory -- Figure 2. A new dataset on NPL dynamics -- Figure 3. NPLs during banking crises around the world -- Figure 4. Distribution of peak NPLs -- Figure 5. Time to peak -- Figure 6. NPL resolution within 7 years -- Figure 7. Time to NPL resolution -- Figure 8. Output dynamics -- Figure 9. European Crisis Experience
Literary Form
non fiction
Physical Description
1 online resource (41 pages)
Specific Material Designation
Form Of Item

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