Global Portfolio Credit Risk Management: The US Banks Post-Crisis Challenge
This paper addresses the problem of modeling credit risk for multi-product and global loan portfolios. The authors presented an improved version of the Basel Committee’s one-factor model for capital requirements calculation. They examined whether latent market factors corresponding to distinct portf...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-03-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/9/5/562 |
| _version_ | 1797413150220353536 |
|---|---|
| author | Pawel Siarka |
| author_facet | Pawel Siarka |
| author_sort | Pawel Siarka |
| collection | DOAJ |
| description | This paper addresses the problem of modeling credit risk for multi-product and global loan portfolios. The authors presented an improved version of the Basel Committee’s one-factor model for capital requirements calculation. They examined whether latent market factors corresponding to distinct portfolios are always highly correlated within the global portfolio and how this correlation impacts total losses distribution function. Historical losses of top-tier banks (JPMorgan Chace, Bank of America, Citigroup, Wells Fargo, US Bancorp) were analyzed. Furthermore, the estimation of the correlations between latent market factors was conducted, and its impact on the total loss distribution function was assessed. The research was performed based on consolidated financial statements for holding companies - FR Y-9C reports provided by the Federal Reserve Bank of Chicago. To verify the improved model, the authors analyzed two distinct loan portfolios for each bank, i.e., credit cards and commercial and industrial loans. They showed that the correlation between latent market factors could be significantly lower than one and disregarding this conclusion may lead to overestimating total unexpected losses. Hence, capital requirements calculated according to the IRB (Internal Ratings Based Approach) formula as a sum of individual VaR999 estimates may be biased. According to this finding, the enhanced one-factor model seems to be more accurate while calculating unexpected total loss for global portfolios. The authors proved that the active credit risk management process aiming to lower market factors’ correlation results in less volatile total losses. Therefore, financial institutions could be more resistant to macroeconomic downturns. |
| first_indexed | 2024-03-09T05:13:28Z |
| format | Article |
| id | doaj.art-d2364a448abb4476ae34754cb7773eb4 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T05:13:28Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-d2364a448abb4476ae34754cb7773eb42023-12-03T12:47:27ZengMDPI AGMathematics2227-73902021-03-019556210.3390/math9050562Global Portfolio Credit Risk Management: The US Banks Post-Crisis ChallengePawel Siarka0Department of Accounting, Reporting and Financial Analysis, Wroclaw University of Economics and Business, 53-345 Wroclaw, PolandThis paper addresses the problem of modeling credit risk for multi-product and global loan portfolios. The authors presented an improved version of the Basel Committee’s one-factor model for capital requirements calculation. They examined whether latent market factors corresponding to distinct portfolios are always highly correlated within the global portfolio and how this correlation impacts total losses distribution function. Historical losses of top-tier banks (JPMorgan Chace, Bank of America, Citigroup, Wells Fargo, US Bancorp) were analyzed. Furthermore, the estimation of the correlations between latent market factors was conducted, and its impact on the total loss distribution function was assessed. The research was performed based on consolidated financial statements for holding companies - FR Y-9C reports provided by the Federal Reserve Bank of Chicago. To verify the improved model, the authors analyzed two distinct loan portfolios for each bank, i.e., credit cards and commercial and industrial loans. They showed that the correlation between latent market factors could be significantly lower than one and disregarding this conclusion may lead to overestimating total unexpected losses. Hence, capital requirements calculated according to the IRB (Internal Ratings Based Approach) formula as a sum of individual VaR999 estimates may be biased. According to this finding, the enhanced one-factor model seems to be more accurate while calculating unexpected total loss for global portfolios. The authors proved that the active credit risk management process aiming to lower market factors’ correlation results in less volatile total losses. Therefore, financial institutions could be more resistant to macroeconomic downturns.https://www.mdpi.com/2227-7390/9/5/562credit riskprobability of defaultone-factor modelstress tests |
| spellingShingle | Pawel Siarka Global Portfolio Credit Risk Management: The US Banks Post-Crisis Challenge Mathematics credit risk probability of default one-factor model stress tests |
| title | Global Portfolio Credit Risk Management: The US Banks Post-Crisis Challenge |
| title_full | Global Portfolio Credit Risk Management: The US Banks Post-Crisis Challenge |
| title_fullStr | Global Portfolio Credit Risk Management: The US Banks Post-Crisis Challenge |
| title_full_unstemmed | Global Portfolio Credit Risk Management: The US Banks Post-Crisis Challenge |
| title_short | Global Portfolio Credit Risk Management: The US Banks Post-Crisis Challenge |
| title_sort | global portfolio credit risk management the us banks post crisis challenge |
| topic | credit risk probability of default one-factor model stress tests |
| url | https://www.mdpi.com/2227-7390/9/5/562 |
| work_keys_str_mv | AT pawelsiarka globalportfoliocreditriskmanagementtheusbankspostcrisischallenge |