The Art of credit derivates shows practitioners how to put a frame work in place which will support the securitization activity. by showing the models that support this activity and linking them with very practical examples, the authors show why a mind–shift within the quant community is needed – a move from simple modeling to a more hand son mindset where the modeler understands the trading implicitly.
The book has been written in five parts, covering the modeling framework; single name corporate credit derivatives; multi name corporate credit derivatives; asset backed securities and dynamic credit portfolio management.
- groundbreaking solutions to the inherent risks associated with investing in securitization instruments
- how to use the standardized credit indices as the most appropriate instruments in price discovery processes and why these indices are the essential tools for short term credit portfolio management
- why the dynamics of systemic correlation and the standardized credit indices are linked with leverage, and consequently the implications for liquidity and solvability of financial institutions
- how Levy processes and long term memory processes are related to the understanding of economic activity
- why regulatory capital should be portfolio dependent and how to use stress tests and scenario analysis to model this
- how to put structured products in a mark–to market–environment, increasing transparency for accounting and compliance.
This book will be invaluable reading for Credit Analysts, Quantitative Analysts, credit Portfolio Mangers, Academics and anyone interested in these complex yet important markets.
List of Tables.
List of Figures.
PART I MODELING FRAMEWORK.
2 Default Models.
2.3 Default Models.
3 Modeling Dependence with Copulas.
3.3 Using Copulas in Practice and Factor Analysis.
PART II SINGLE NAME CORPORATE CREDIT DERIVATIVES.
4 Credit Default Swaps.
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 Protocol.
5 Pricing Credit Spread Options: A 2–factor HW–BK Algorithm.
5.2 The Credit Event Process.
5.3 Credit Spread Options.
5.4 Hull White and Black Karazinsky Models.
6 Counterparty Risk and Credit Valuation Adjustment.
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.
PART III MULTINAME CORPORATE CREDIT DERIVATIVES.
7 Collateralized Debt Obligations.
7.2 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.
8.2 Credit Default Swap Indices.
8.4 iTraxx, CDX and their Tranches.
8.5 Theoretical Fair Spread of Indices.
9 Pricing Synthetic CDO Tranches.
9.2 Generic 1–Factor Model.
9.3 Implied Compound and Base Correlation.
10 Historical Study of Lévy Base Correlation.
10.2 Historical Study.
10.3 Base Correlation.
10.4 Hedge Parameters.
11 Base Expected Loss and Base Correlation Smile.
11.2 Base Correlation and Expected Loss: Intuition.
11.3 Base Correlation and Interpolation.
11.4 Base Expected Loss.
11.6 Numerical Results.
12 Base Correlation Mapping.
12.2 Correlation Mapping for Bespoke Portfolios.
12.3 Numerical Results.
12.4 Final Comments.
13 Correlation from Collateral to Tranches.
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 Correlations.
13.6 Marshall–Olkin Copula Tranche Loss Correlations.
14 Cash Flow CDOs.
14.2 The Waterfall of a Cash Flow CDO.
14.3 BET Methodology.
14.5 AIG and BET.
15 Structured Credit Products: CPPI and CPDO.
15.2 Multivariate VG Modeling.
15.3 Swaptions on Credit Indices.
15.4 Model Calibration.
PART IV ASSET BACKED SECURITIES.
16 ABCDS and PAUG.
16.2 ABCDSs versus Corporate CDSs.
16.3 ABCDS Pay As You Go: PAUG.
17 One Credit Event Models for CDOs of ABS.
17.2 ABS Bond and ABCDS.
17.3 Single Name Sensitivity.
17.4 Multifactor Correlation Model.
17.5 Monte Carlo Simulation.
18 More Standardized Credit Indices: ABX, TABX, CMBX, LCDX, LevX.
18.2 ABX and TABX.
18.3 LevX and LCDX.
18.4 CMBX and ECMBX.
18.5 Indices as Indicators.
19 1–factor Models for the ABS Correlation Market Pricing TABX Tranches.
19.2 Generic 1–factor Model.
19.3 Amortizing Bond and CDS.
19.4 A Simple Model for Amortization and Prepayment.
19.5 ABX.HE Credit Index.
19.6 Prepayment and Model Calibration.
19.7 Pricing Model Implications.
20 Bond Price Implied Spreads.
20.2 Bond Price Implied Spreads.
20.3 Numerical Results.
PART V DYNAMIC CREDIT PORTFOLIO MANAGEMENT.
21 Long Memory Processes and Benoit Mandelbrot.
21.2 Econophysics, Fat Tails and Brownian Motion.
21.3 Long–term Memory and the Nile River.
21.4 Capital Asset Pricing Model.
22 Securitization and the Credit Crunch.
22.2 Correlation and Mortgage–backed Securities.
22.3 Securitization and Economic Growth.
23 Dynamic Credit Portfolio Management.
23.2 Regulatory Capital and Basel Formulas.
23.3 Portfolio Credit Risk and Economic Capital.
23.4 Securitization and CDO Models.
23.5 CDO Pricing.
23.6 Credit Portfolio Management and Correlation Mapping.
23.7 Strategic Credit ECAP Management.
Appendix A: Economic Capital Allocation Approaches.
Appendix B: Generalized Gauss Laguerre Quadrature.