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Credit correlation : life after copulas /
The recent growth of credit derivatives has been explosive. The global credit derivatives market grew in notional value from $1 trillion to $20 trillion from 2000 to 2006. However, understanding the true nature of these instruments still poses both theoretical and practical challenges. For a long ti...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New Jersey :
World Scientific,
©2008.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Credit correlation : |b life after copulas / |c editors, Alexander Lipton, Andrew Rennie. |
| 246 | 3 | 0 | |a Life after copulas |
| 260 | |a New Jersey : |b World Scientific, |c ©2008. | ||
| 300 | |a 1 online resource (vii, 169 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a The recent growth of credit derivatives has been explosive. The global credit derivatives market grew in notional value from $1 trillion to $20 trillion from 2000 to 2006. However, understanding the true nature of these instruments still poses both theoretical and practical challenges. For a long time now, the framework of Gaussian copulas parameterized by correlation, and more recently base correlation, has provided an adequate, if unintuitive, description of the market. However, the increased liquidity in credit indices and index tranches, as well as the proliferation of exotic instruments such as forward starting tranches, options on tranches, leveraged super senior tranches, and the like, have made it imperative to come up with models that describe market reality better. This book, originally and concurrently published in the International Journal of Theoretical and Applied Finance, Vol. 10, No. 4, 2007, agrees that base correlation has outlived its usefulness; opinions of how to replace it, however, are divided. Both the top-down and bottom-up approaches for describing the dynamics of credit baskets are presented, and pro and contra arguments are put forward. Readers will decide which direction is the most promising one at the moment. However, it is hoped that, in the near future, models that transcend base correlation will be proposed and accepted by the market. | ||
| 505 | 0 | |a Introduction; Levy Simple Structural Models M. Baxter; 1. Introduction; 2. Levy Processes; 3. Credit Models for Single Names; 3.1. Example: Term structure of a single credit; 3.2. Extensions; 4. Portfolio Credit Models; 5. Calibration and Model Comparison; 6. Parameter Risks and Hedging; 6.1. Case study: Auto crisis May 2005; 7. Implementation and Other Products; 7.1. Calculating the distribution function; 7.2. Performing the optimization; 7.3. Other products; 8. Summary and Conclusions; References | |
| 505 | 8 | |a Cluster-Based Extension of the Generalized Poisson Loss Dynamics and Consistency with Single Names D. Brigo, A. Pallavicini and R. Torresetti1. Introduction; 2. Modeling Framework and the CPS Approach; 3. Avoiding Repeated Defaults; 3.1. Default-counting adjustment: GPL model (Strategy 0); 3.2. Single-name adjusted approach (Strategy 1); 3.3. GPCL model: Cluster-adjusted approach (Strategy 2); 3.4. Comparing models in a simplified scenario; 4. The GPCL Model Calibration; 4.1. Calibration results; 5. Extensions: Spread and Recovery Dynamics; 6. Conclusions; Acknowledgements; References | |
| 505 | 8 | |a Appendix A. Market QuotesAppendix B. Calibration Inputs and Outputs; Stochastic Intensity Modeling for Structured Credit Exotics A. Chapovsky, A. Rennie and P. Tavares; 1. Introduction; 2. Model Setup; 2.1. Motivation; 2.2. Single credit dynamics; 2.3. Multiple credit dynamics; 2.4. Factorization of intensity dynamics; 2.5. Note on credit correlation; 3. Model Parametrization and Calibration; 3.1. Jump-only process; 3.2. Jump-CIR process; 3.3. Non-linear jump-diffusion process; 3.4. Idiosyncratic intensity dynamics; 4. Application to Structured Credit Exotics | |
| 505 | 8 | |a 4.1. Approximating model dynamics4.2. Pricing of derivatives; 4.2.1. Vanilla tranches; 4.2.2. European option on tranche; 4.2.3. Leveraged tranche; 4.2.4. Tranche with counterparty risk; 5. Conclusions; Acknowledgments; References; Large Portfolio Credit Risk Modeling M. H. A. Davis and J. C. Esparragoza-Rodriguez; 1. Introduction; 2. Model Description; 2.1. Formal definition of the model; 3. Fluid and Diffusion Limits; 4. Convergence Results for the Rating Distribution Process; 4.1. The fiuid limit; 4.2. The diffusion limit | |
| 505 | 8 | |a 4.3. The infinitesimal generator of the single-obligor process and the probability of default5. Computational Aspects: Quadratures; 5.1. CDO pricing; 5.2. Changes of measure, the Poisson space and Quadrature formulas; 5.2.1. The canonical space of a Poisson process; 5.2.2. Gaussian quadratures; 5.3. Some comparisons; 6. Calibration; 6.1. A 3-state environment process; 6.1.1. Implementation; 7. Conclusions; References; Empirical Copulas for CDO Tranche Pricing Using Relative Entropy M. A. H. Dempster, E. A. Medova and S. W. Yang; 1. Introduction | |
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Credit derivatives. | |
| 650 | 6 | |a Instruments dérivés de crédit. | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Investments & Securities |x General. |2 bisacsh | |
| 650 | 7 | |a Credit derivatives. |2 fast |0 (OCoLC)fst00882587 | |
| 700 | 1 | |a Lipton, Alexander. | |
| 700 | 1 | |a Rennie, Andrew, |d 1968- | |
| 730 | 0 | |a International journal of theoretical and applied finance. | |
| 776 | 0 | 8 | |i Print version: |t Credit correlation. |d New Jersey : World Scientific, ©2008 |w (DLC) 2008273169 |
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