The art of credit derivatives : demystifying the black swan /

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Derivatives have been instrumental in the recent increase in securitization activity. The complex nature and the size of the market have given rise to very complex counterparty credit risks. The Lehman failure has shown that these issues can paralyse the financial markets, and the need for detailed...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Garcia, João
Otros Autores: Goossens, Serge
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Chichester, West Sussex, U.K. : Wiley, ©2010.
Colección:Wiley finance series.
Temas:
Credit derivatives.
Securities.
Portfolio management.
Instruments dérivés de crédit.
Gestion de portefeuille.
BUSINESS & ECONOMICS > Finance.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • The Art of Credit Derivatives: Demystifying the Black Swan; Contents; About the Authors; Acknowledgements; Preface; List of Tables; List of Figures; 1 Introduction; PART I MODELING FRAMEWORK; 2 Default Models; 2.1 Introduction; 2.2 Default; 2.3 Default Models; 3 Modeling Dependence with Copulas; 3.1 Introduction; 3.2 Copula; 3.3 Using Copulas in Practice and Factor Analysis; PART II SINGLE NAME CORPORATE CREDIT DERIVATIVES; 4 Credit Default Swaps; 4.1 Introduction; 4.2 Credit Default Swap: A Description; 4.3 Modeling CDSs; 4.4 Calibrating the Survival Probability; 4.5 2008 Auction Results
  • 4.6 The Big Bang Protocol5 Pricing Credit Spread Options: A 2-factor HW-BK Algorithm; 5.1 Introduction; 5.2 The Credit Event Process; 5.3 Credit Spread Options; 5.4 Hull-White and Black-Karazinsky Models; 5.5 Results; 5.6 Conclusion; 6 Counterparty Risk and Credit Valuation Adjustment; 6.1 Introduction; 6.2 Valuation of the CVA; 6.3 Monte Carlo Simulation for CVA on CDS; 6.4 Semi-analytic Correlation Model; 6.5 Numerical Results; 6.6 CDS with Counterparty Risk; 6.7 Counterparty Risk Mitigation; 6.8 Conclusions; PART III MULTINAME CORPORATE CREDIT DERIVATIVES; 7 Collateralized Debt Obligations
  • 7.1 Introduction7.2 A Brief Overview of CDOs; 7.3 Cash versus Synthetic CDOs; 7.4 Synthetic CDOs and Leverage; 7.5 Concentration, Correlation and Diversification; 8 Standardized Credit Indices; 8.1 Introduction; 8.2 Credit Default Swap Indices; 8.3 Standardization; 8.4 iTraxx, CDX and their Tranches; 8.5 Theoretical Fair Spread of Indices; 9 Pricing Synthetic CDO Tranches; 9.1 Introduction; 9.2 Generic 1-Factor Model; 9.3 Implied Compound and Base Correlation; 10 Historical Study of Lévy Base Correlation; 10.1 Introduction; 10.2 Historical Study; 10.3 Base Correlation; 10.4 Hedge Parameters
  • 10.5 Conclusions11 Base Expected Loss and Base Correlation Smile; 11.1 Introduction; 11.2 Base Correlation and Expected Loss: Intuition; 11.3 Base Correlation and Interpolation; 11.4 Base Expected Loss; 11.5 Interpolation; 11.6 Numerical Results; 11.7 Conclusions; 12 Base Correlation Mapping; 12.1 Introduction; 12.2 Correlation Mapping for Bespoke Portfolios; 12.3 Numerical Results; 12.4 Final Comments; 13 Correlation from Collateral to Tranches; 13.1 Introduction; 13.2 Generic 1-Factor Model; 13.3 Monte Carlo Simulation and Importance Sampling; 13.4 Gaussian Copula Tranche Loss Correlations
  • 13.5 Lévy Copula Tranche Loss Correlations13.6 Marshall-Olkin Copula Tranche Loss Correlations; 13.7 Conclusions; 14 Cash Flow CDOs; 14.1 Introduction; 14.2 The Waterfall of a Cash Flow CDO; 14.3 BET Methodology; 14.4 Results; 14.5 AIG and BET; 14.6 Conclusions; 15 Structured Credit Products: CPPI and CPDO; 15.1 Introduction; 15.2 Multivariate VG Modeling; 15.3 Swaptions on Credit Indices; 15.4 Model Calibration; 15.5 CPPI; 15.6 CPDO; 15.7 Conclusion; PART IV ASSET BACKED SECURITIES; 16 ABCDS and PAUG; 16.1 Introduction; 16.2 ABCDSs versus Corporate CDSs; 16.3 ABCDS Pay As You Go: PAUG

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