Impact of the Industries, Behavioral Information, and Covid-19 Measures on the Non-financial Corporates Default Rates

Author:

Palán Luděk

Publisher

Springer Nature Switzerland

Reference20 articles.

1. Alonso, A., & Carbó, J. M. (2020). Machine learning in credit risk: Measuring the dilemma between prediction and supervisory cost. Banco de Espana, Working Paper Series: https://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTraajo/20/Files/dt2032e.pdf

2. Babouček, I., & Jančar, M. (2005). A VAR analysis of the effects of macroeconomic shocks to the quality of the aggregate loan Portfolio of the Czech Banking Sector (ČNB Working paper series). Načteno z https://www.cnb.cz/export/sites/cnb/en/economic-research/.galleries/research_publications/cnb_wp/cnbwp_2005_01.pdf

3. Bellini, T. (2019). IFRS 9 and CECL credit risk modelling and validation: a practical guide with examples worked in R and SAS. Academic.

4. Bykova, A., & Pindyuk, O. (2019). Non-performing loans in central and Southeast Europe. Policy Notes and Reports, No. 32.

5. De Bock, R., & Demyanets , A. (2012). Bank asset quality in emerging markets: Determinants and spillovers. IMF (Working Paper 12/7).

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