CreditRisk+ A Credit Risk Management Framework. CREDITRISK+ is based on a portfolio approach to modelling credit default risk that takes into account information relating to size and maturity of an exposure and the credit quality and systematic risk of an obligor. The CREDITRISK+ Model is a statistical model of credit default risk that makes no assumptions about the causes of default. This approach is similar to that taken in market risk management, where no attempt is made to model the causes of market price movements. The CREDITRISK+ Model considers default rates as continuous random variables and incorporates the volatility of default rates in order to capture the uncertainty in the level of default rates. Often, background factors, such as the state of the economy, may cause the incidence of defaults to be correlated, even though there is no causal link between them. The effects of these background factors are incorporated into the CREDITRISK+ Model through the use of default rate volatilities and sector analysis rather than using default correlations as explicit inputs into the model. Mathematical techniques applied widely in the insurance industry are used to model the sudden event of an obligor default. This approach contrasts with the mathematical techniques typically used in finance. In financial modelling one is usually concerned with modelling continuous price changes rather than sudden events. Applying insurance modelling techniques, the analytic CREDITRISK+ Model captures the essential characteristics of credit default events and allows explicit calculation of a full loss distribution for a portfolio of credit exposures.

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  1. Kim, Joseph H. T.; Jang, Jiwook; Pyun, Chaehyun: Capital allocation for a sum of dependent compound mixed Poisson variables: a recursive algorithm approach (2019)
  2. Li, Chen; Li, Xiaohu: Preservation of WSAI under default transforms and its application in allocating assets with dependent realizable returns (2019)
  3. Qu, Yan; Dassios, Angelos; Zhao, Hongbiao: Efficient simulation of Lévy-driven point processes (2019)
  4. Başoğlu, İsmail; Hörmann, Wolfgang; Sak, Halis: Efficient simulations for a Bernoulli mixture model of portfolio credit risk (2018)
  5. Bonollo, Michele; Di Persio, Luca; Prezioso, Luca: The default risk charge approach to regulatory risk measurement processes (2018)
  6. Fernández-Sánchez, Juan; Úbeda-Flores, Manuel: Constructions of copulas with given diagonal (and opposite diagonal) sections and some generalizations (2018)
  7. Fu, Miaoqi; Peng, Xianhua: On the sample path properties of mixed Poisson processes (2018)
  8. Puccetti, Giovanni; Scherer, Matthias: Copulas, credit portfolios, and the broken heart syndrome. An interview with David X. Li (2018)
  9. Zhou, Ming; Dhaene, Jan; Yao, Jing: An approximation method for risk aggregations and capital allocation rules based on additive risk factor models (2018)
  10. Ackerer, Damien; Vatter, Thibault: Dependent defaults and losses with factor copula models (2017)
  11. Chen, Zhijin; Yang, Jingping; Wang, Xiaoqian: Pricing (k)th realization derivatives and collateralized debt obligation with multivariate Fréchet copula (2016)
  12. Fischer, Matthias; Jakob, Kevin: pTAS distributions with application to risk management (2016)
  13. Fischer, Matthias; Köstler, Christoph; Jakob, Kevin: Modeling stochastic recovery rates and dependence between default rates and recovery rates within a generalized credit portfolio framework (2016)
  14. Han, Xiaoying; Wang, Ruodu: Computation of credit portfolio loss distribution by a cross entropy method (2016)
  15. Szotek, Jakub: Generalized CreditRisk(^+) model and applications (2015)
  16. Wang, Ruodu; Peng, Liang; Yang, Jingping: CreditRisk(^+) model with dependent risk factors (2015)
  17. Chen, Rongda; Yu, Huanhuan: Risk measurement for portfolio credit risk based on a mixed Poisson model (2014)
  18. Deshpande, Amogh: Comparing the value at risk performance of the CreditRisk(^+) and its enhancement: a large deviations approach (2014)
  19. Hong, L. Jeff; Juneja, Sandeep; Luo, Jun: Estimating sensitivities of portfolio credit risk using Monte Carlo (2014)
  20. Jakob, Kevin; Fischer, Matthias: Quantifying the impact of different copulas in a generalized CreditRisk(^+) framework. An empirical study (2014)

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