| Author(s): |
Ing. Mgr. Barbora Štěpánková M.A., Ph.D.,
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| Type: |
Articles in journals with impact factor |
| Year: |
2021 |
| Number: |
0 |
| ISSN / ISBN: |
DOI: 10.1016/j.ejor.2020.06.024 |
| Published in: |
European Journal of Operational Research |
| Publishing place: |
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| Keywords: |
Risk management, Credit risk, Transition matrices, Simulation, Banking |
| JEL codes: |
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| Suggested Citation: |
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| Grants: |
GACR 18-05244S - Innovative Approaches to Credit Risk Management
PRIMUS/19/HUM/17 2019-2021 Behavioral finance and macroeconomics: New insights for the mainstream
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| Abstract: |
This study proposes and analyses a novel alternative to credit transition matrices (CTMs) developed by credit rating agencies - bank-sourced CTMs. It provides a unique insight into estimation of bank-sourced CTMs by assessing the extent to which the CTMs depend on the characteristics of the underlying credit risk datasets and the aggregation method and outlines that the choice of aggregation approach has a substantial effect on credit risk model results. Further, we show that bank-sourced CTMs are more dynamic than those of credit rating agencies, with higher off-diagonal transition rates and higher propensity to upgrade. Finally, we create a set of industry-specific CTMs, otherwise unobtainable due to the data sparsity faced by credit rating agencies, and highlight the implications of their differences, signalling the existence of industry-specific business cycles. The study uses a unique and large dataset of internal credit risk estimates from 24 global banks covering monthly observations on more than 26,000 large corporates and employs large-scale Monte Carlo simulations. This approach can be replicated by regulators (e.g., data collected by the European Central Bank in the AnaCredit project) and used by organisations aiming to improve their credit risk models. |
