LEADER 00886nam0-22003131i-450- 001 990003689130403321 010 $a0-387-98898-X 035 $a000368913 035 $aFED01000368913 035 $a(Aleph)000368913FED01 035 $a000368913 100 $a20000920d2000----km-y0itay50------ba 101 0 $aENG 200 1 $aPermutation tests$ea practical guide to resampling methods for testing hypotheses$fPhillip Good 205 $a2nd ed 210 $aNew York$cSpringer$dc2000 215 $a270 p.$d24 cm 225 1 $aSpringer series in statistics 610 0 $aInferenza statistica 610 0 $aTeoria delle decisioni 676 $a519.56 700 1$aGood,$bPhillip$0102489 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003689130403321 952 $aIX-C-37$b7904$fMAS 959 $aMAS 996 $aPermutation Tests$9439181 997 $aUNINA LEADER 01408nam--2200397---450 001 990001109990203316 005 20180312154824.0 010 $a3-540-43287-6 035 $a000110999 035 $aUSA01000110999 035 $a(ALEPH)000110999USA01 035 $a000110999 100 $a20021202d2002----km-y0ITAy0103-------ba 101 0 $aENG 102 $aDE 200 1 $aTypes for proofs and programs$eInternational Workshop, TYPES 2000$eDurham ,UK, December 8-12, 2000$eSelected papers$fPaul Callaghan... (eds.) 210 $aBerlin [etc.]$cSpringer-Verlag$dcopyr. 2002 215 $a242 p.$cill.$d20 cm 225 $aLecture notes in computer science$v2277 410 $12001$aLecture notes in computer science$v2277 610 1 $aLogica matematica$aCongressi$a2000 676 $a511.30285 702 1$aCALLAGHAN,$bPaul 710 12$aInternational workshop, TYPES 2000 <2000 ; Durham ; UK >$0554397 801 0$aITA$bCBS$gISBD 912 $a990001109990203316 951 $a001 LNCS (2277)$b0026778 CBS$c001$d00112123 959 $aBK 969 $aSCI 979 $aDIGIUSEPPE$b90$c20021202$lUSA01$h1155 979 $aDIGIUSEPPE$b90$c20021202$lUSA01$h1428 979 $aDIGIUSEPPE$b90$c20021202$lUSA01$h1430 979 $aPATRY$b90$c20040406$lUSA01$h1717 979 $aFIORELLA$b90$c20070322$lUSA01$h1606 996 $aTypes for proofs and programs$9981087 997 $aUNISA LEADER 05206nam 22006374a 450 001 9910451487003321 005 20200520144314.0 010 $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 608 $aElectronic books. 615 0$aCredit derivatives. 676 $a332.64/57 701 $aLipton$b Alexander$0942400 701 $aRennie$b Andrew$f1968-$0614489 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910451487003321 996 $aCredit correlation$92126606 997 $aUNINA