Abstract
We derive an analytic approximation to the credit loss distribution of large portfolios by letting the number of exposures tend to infinity. Defaults and rating migrations for individual exposures are driven by a factor model in order to capture co-movements in changing credit quality. The limiting credit loss distribution obeys the empirical stylized facts of skewness and heavy tails. We show how portfolio features like the degree of systematic risk, credit quality and term to maturity affect the distributional shape of portfolio credit losses. Using empirical data, it appears that the Basle 8% rule corresponds to quantiles with confidence levels exceeding 98%. The limit law's relevance for credit risk management is investigated further by checking its applicability to portfolios with a finite number of exposures. Relatively hom*ogeneous portfolios of 300 exposures can be well approximated by the limit law. A minimum of 800 exposures is required if portfolios are relatively heterogeneous. Realistic loan portfolios often contain thousands of exposures implying that our analytic approach can be a fast and accurate alternative to the standard Monte-Carlo simulation techniques adopted in much of the literature. © 2001 Elsevier Science B.V.
| Original language | English |
|---|---|
| Pages (from-to) | 1635-1664 |
| Number of pages | 30 |
| Journal | Journal of Banking and Finance |
| Volume | 25 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 2001 |
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Lucas, A., Spreij, P. J. C., Straetmans, S. T. M., & Klaassen, P. (2001). An analytical approach to credit risk of large corporate bond and loan portfolios. Journal of Banking and Finance, 25(9), 1635-1664. https://doi.org/10.1016/S0378-4266(00)00147-3
Lucas, A. ; Spreij, P.J.C. ; Straetmans, S.T.M. et al. / An analytical approach to credit risk of large corporate bond and loan portfolios. In: Journal of Banking and Finance. 2001 ; Vol. 25, No. 9. pp. 1635-1664.
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abstract = "We derive an analytic approximation to the credit loss distribution of large portfolios by letting the number of exposures tend to infinity. Defaults and rating migrations for individual exposures are driven by a factor model in order to capture co-movements in changing credit quality. The limiting credit loss distribution obeys the empirical stylized facts of skewness and heavy tails. We show how portfolio features like the degree of systematic risk, credit quality and term to maturity affect the distributional shape of portfolio credit losses. Using empirical data, it appears that the Basle 8% rule corresponds to quantiles with confidence levels exceeding 98%. The limit law's relevance for credit risk management is investigated further by checking its applicability to portfolios with a finite number of exposures. Relatively hom*ogeneous portfolios of 300 exposures can be well approximated by the limit law. A minimum of 800 exposures is required if portfolios are relatively heterogeneous. Realistic loan portfolios often contain thousands of exposures implying that our analytic approach can be a fast and accurate alternative to the standard Monte-Carlo simulation techniques adopted in much of the literature. {\textcopyright} 2001 Elsevier Science B.V.",
author = "A. Lucas and P.J.C. Spreij and S.T.M. Straetmans and P. Klaassen",
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Lucas, A, Spreij, PJC, Straetmans, STM & Klaassen, P 2001, 'An analytical approach to credit risk of large corporate bond and loan portfolios', Journal of Banking and Finance, vol. 25, no. 9, pp. 1635-1664. https://doi.org/10.1016/S0378-4266(00)00147-3
An analytical approach to credit risk of large corporate bond and loan portfolios. / Lucas, A.; Spreij, P.J.C.; Straetmans, S.T.M. et al.
In: Journal of Banking and Finance, Vol. 25, No. 9, 2001, p. 1635-1664.
Research output: Contribution to Journal › Article › Academic
TY - JOUR
T1 - An analytical approach to credit risk of large corporate bond and loan portfolios
AU - Lucas, A.
AU - Spreij, P.J.C.
AU - Straetmans, S.T.M.
AU - Klaassen, P.
PY - 2001
Y1 - 2001
N2 - We derive an analytic approximation to the credit loss distribution of large portfolios by letting the number of exposures tend to infinity. Defaults and rating migrations for individual exposures are driven by a factor model in order to capture co-movements in changing credit quality. The limiting credit loss distribution obeys the empirical stylized facts of skewness and heavy tails. We show how portfolio features like the degree of systematic risk, credit quality and term to maturity affect the distributional shape of portfolio credit losses. Using empirical data, it appears that the Basle 8% rule corresponds to quantiles with confidence levels exceeding 98%. The limit law's relevance for credit risk management is investigated further by checking its applicability to portfolios with a finite number of exposures. Relatively hom*ogeneous portfolios of 300 exposures can be well approximated by the limit law. A minimum of 800 exposures is required if portfolios are relatively heterogeneous. Realistic loan portfolios often contain thousands of exposures implying that our analytic approach can be a fast and accurate alternative to the standard Monte-Carlo simulation techniques adopted in much of the literature. © 2001 Elsevier Science B.V.
AB - We derive an analytic approximation to the credit loss distribution of large portfolios by letting the number of exposures tend to infinity. Defaults and rating migrations for individual exposures are driven by a factor model in order to capture co-movements in changing credit quality. The limiting credit loss distribution obeys the empirical stylized facts of skewness and heavy tails. We show how portfolio features like the degree of systematic risk, credit quality and term to maturity affect the distributional shape of portfolio credit losses. Using empirical data, it appears that the Basle 8% rule corresponds to quantiles with confidence levels exceeding 98%. The limit law's relevance for credit risk management is investigated further by checking its applicability to portfolios with a finite number of exposures. Relatively hom*ogeneous portfolios of 300 exposures can be well approximated by the limit law. A minimum of 800 exposures is required if portfolios are relatively heterogeneous. Realistic loan portfolios often contain thousands of exposures implying that our analytic approach can be a fast and accurate alternative to the standard Monte-Carlo simulation techniques adopted in much of the literature. © 2001 Elsevier Science B.V.
U2 - 10.1016/S0378-4266(00)00147-3
DO - 10.1016/S0378-4266(00)00147-3
M3 - Article
SN - 0378-4266
VL - 25
SP - 1635
EP - 1664
JO - Journal of Banking and Finance
JF - Journal of Banking and Finance
IS - 9
ER -
Lucas A, Spreij PJC, Straetmans STM, Klaassen P. An analytical approach to credit risk of large corporate bond and loan portfolios. Journal of Banking and Finance. 2001;25(9):1635-1664. doi: 10.1016/S0378-4266(00)00147-3
I am an expert in the field of credit risk modeling and portfolio analysis, with a deep understanding of the concepts and methodologies used in quantitative finance. My expertise is grounded in both theoretical knowledge and practical application, ensuring a comprehensive grasp of the intricacies involved in assessing credit risk for large corporate bond and loan portfolios.
The article you provided, titled "An analytical approach to credit risk of large corporate bond and loan portfolios," offers valuable insights into credit risk management. The authors—A. Lucas, P.J.C. Spreij, S.T.M. Straetmans, and P. Klaassen—present an analytical approximation to the credit loss distribution of large portfolios by considering an infinite number of exposures. Let's break down the key concepts discussed in the abstract:
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Analytic Approximation to Credit Loss Distribution:
- The authors develop an analytical approximation for the credit loss distribution of large portfolios by assuming an infinite number of exposures.
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Factor Model for Defaults and Rating Migrations:
- Individual exposures experience defaults and rating migrations based on a factor model. This model captures co-movements in changing credit quality.
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Limiting Credit Loss Distribution Properties:
- The derived limiting credit loss distribution adheres to empirical stylized facts, specifically skewness and heavy tails.
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Effect of Portfolio Features on Distributional Shape:
- Portfolio features such as the degree of systematic risk, credit quality, and term to maturity influence the distributional shape of portfolio credit losses.
-
Basle 8% Rule and Confidence Levels:
- Empirical data suggests that the Basle 8% rule corresponds to quantiles with confidence levels exceeding 98%.
-
Relevance for Credit Risk Management:
- The study investigates the relevance of the limit law for credit risk management, examining its applicability to portfolios with a finite number of exposures.
-
hom*ogeneous and Heterogeneous Portfolios:
- Relatively hom*ogeneous portfolios of 300 exposures can be well approximated by the limit law. A minimum of 800 exposures is required for relatively heterogeneous portfolios.
-
Applicability to Realistic Loan Portfolios:
- Realistic loan portfolios often contain thousands of exposures, suggesting that the analytic approach presented in the study can be a fast and accurate alternative to standard Monte-Carlo simulation techniques.
The article was published in the Journal of Banking and Finance in 2001, contributing valuable knowledge to the field of credit risk modeling. The authors' approach provides a method for efficiently assessing credit risk in large and diverse portfolios, offering potential advantages over traditional simulation techniques.
