02435nmm a2200325 u 4500001001200000003002700012005001700039007002400056008004100080020001800121100002100139245009500160250001700255260006300272300004200335505040700377653002400784653001200808653002500820653002800845653003000873653003200903653002100935041001900956989003600975490005601011856007201067082000801139520096201147EB000385255EBX0100000000000000023830700000000000000.0cr|||||||||||||||||||||130626 ||| eng a97836421560901 aSchlösser, Anna00aPricing and Risk Management of Synthetic CDOshElektronische Ressourcecby Anna Schlösser a1st ed. 2011 aBerlin, HeidelbergbSpringer Berlin Heidelbergc2011, 2011 aXII, 268 p. 90 illusbonline resource0 aIntroduction -- Part I Fundamentals: Credit Derivatives and Markets -- Mathematical Preliminaries -- Part II Static Models: One Factor Gaussian Copula Model -- Normal Inverse Gaussian Factor Copula Model -- Part III: Term-Structure Models -- Large Homogeneous Cell Approximation for Factor Copula Models -- Regime-Switching Extension of the NIG Factor Copula Model -- Simulation Framework -- Conclusion aApplied mathematics aFinance aQuantitative Finance aEngineering mathematics aEconomics, Mathematical aApplications of Mathematics aFinance, general07aeng2ISO 639-2 bSpringeraSpringer eBooks 2005-0 aLecture Notes in Economics and Mathematical Systems40uhttps://doi.org/10.1007/978-3-642-15609-0?nosfx=yxVerlag3Volltext0 a332 aThis book considers the one-factor copula model for credit portfolios that are used for pricing synthetic CDO structures as well as for risk management and measurement applications involving the generation of scenarios for the complete universe of risk factors and the inclusion of CDO structures in a portfolio context. For this objective, it is especially important to have a computationally fast model that can also be used in a scenario simulation framework. The well known Gaussian copula model is extended in various ways in order to improve its drawbacks of correlation smile and time inconsistency. Also the application of the large homogeneous cell assumption, that allows to differentiate between rating classes, makes the model convenient and powerful for practical applications. The Crash-NIG extension introduces an important regime-switching feature allowing the possibility of a market crash that is characterized by a high-correlation regime