LEADER 05173nam 22006254a 450 001 9910784972103321 005 20230721031122.0 010 $a1-281-91882-2 010 $a9786611918828 010 $a981-270-950-9 035 $a(CKB)1000000000409081 035 $a(EBL)1681339 035 $a(OCoLC)879025125 035 $a(SSID)ssj0000132199 035 $a(PQKBManifestationID)11134621 035 $a(PQKBTitleCode)TC0000132199 035 $a(PQKBWorkID)10028130 035 $a(PQKB)10051386 035 $a(MiAaPQ)EBC1681339 035 $a(WSP)00006559 035 $a(Au-PeEL)EBL1681339 035 $a(CaPaEBR)ebr10255687 035 $a(CaONFJC)MIL191882 035 $a(EXLCZ)991000000000409081 100 $a20080414d2008 uy 0 101 0 $aeng 135 $aurcuu|||uu||| 181 $ctxt 182 $cc 183 $acr 200 00$aCredit correlation$b[electronic resource] $elife after copulas /$feditors, Alexander Lipton, Andrew Rennie 210 $aNew Jersey $cWorld Scientific$dc2008 215 $a1 online resource (178 p.) 300 $aReprinted from the International journal of theoretical and applied finance, v. 10, no. 4 (June 2007). 311 $a981-270-949-5 320 $aIncludes bibliographical references. 327 $aCONTENTS; 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 327 $aCluster-Based Extension of the Generalized Poisson Loss Dynamics and Consistency with Single Names D. Brigo, A. Pallavicini and R. Torresetti 1. 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 327 $aAppendix A. Market Quotes Appendix 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 327 $a4.1. Approximating model dynamics 4.2. 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 327 $a4.3. The infinitesimal generator of the single-obligor process and the probability of default 5. 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 327 $a1.1. Correlated intensities in portfolio credit risk modeling 330 $aThe 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 su 606 $aCredit derivatives 615 0$aCredit derivatives. 676 $a332.64/57 701 $aLipton$b Alexander$01512290 701 $aRennie$b Andrew$f1968-$0614489 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910784972103321 996 $aCredit correlation$93746119 997 $aUNINA