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Credit Securitisations and Derivatives: Challenges for the Global Markets

ISBN: 978-1-119-96396-7
462 pages
May 2013
Credit Securitisations and Derivatives: Challenges for the Global Markets (1119963966) cover image
A comprehensive resource providing extensive coverage of the state of the art in credit secruritisations, derivatives, and risk management

Credit Securitisations and Derivatives is a one-stop resource presenting the very latest thinking and developments in the field of credit risk. Written by leading thinkers from academia, the industry, and the regulatory environment, the book tackles areas such as business cycles; correlation modelling and interactions between financial markets, institutions, and instruments in relation to securitisations and credit derivatives; credit portfolio risk; credit portfolio risk tranching; credit ratings for securitisations; counterparty credit risk and clearing of derivatives contracts and liquidity risk. As well as a thorough analysis of the existing models used in the industry, the book will also draw on real life cases to illustrate model performance under different parameters and the impact that using the wrong risk measures can have.

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Foreword xiii

PART I INTRODUCTION

1 Credit Securitizations and Derivatives 3

1.1 Economic Cycles and Credit Portfolio Risk 3

1.2 Credit Portfolio Risk Measurement 6

1.3 Credit Portfolio Risk Tranching 7

1.4 Credit Ratings 7

1.5 Actuarial vs. Market Credit Risk Pricing 7

1.6 Regulation 8

1.7 Thank You 9

References 9

2 Developments in Structured Finance Markets 11

2.1 Impairments of Asset-Backed Securities and Outstanding Ratings 11

2.2 Issuance of Asset-backed Securities and Outstanding Volume 17

2.3 Global CDO Issuance and Outstanding Volume 19

Concluding Remarks 29

Notes 29

References 31

PART II CREDIT PORTFOLIO RISK MEASUREMENT

3 Mortgage Credit Risk 35

3.1 Introduction 35

3.2 Five “C”s of Credit and Mortgage Credit Risk 38

3.3 Determinants of Mortgage Default, Loss Given Default and Exposure at Default 41

3.3.1 Determinants of Mortgage Default 41

3.3.2 Determinants of Mortgage LGD 43

3.3.3 Determinants of Mortgage EAD 48

3.4 Modeling Methods for Default, LGD and EAD 48

3.5 Model Risk Management 48

3.6 Conclusions 51

References 51

4 Credit Portfolio Correlations and Uncertainty 53

4.1 Introduction 53

4.2 Gaussian and Semi-Gaussian Single Risk Factor Model 54

4.3 Individual and Simultaneous Confidence Bounds and Intervals 55

4.4 Confidence Intervals for Asset Correlations 57

4.5 Confidence Intervals for Default and Survival Time Correlations 59

4.5.1 Confidence Intervals for Default Correlations 60

4.5.2 Confidence Intervals for Survival Time Correlations 61

4.6 Example 63

4.7 Conclusion 65

Appendix 66

Notes 69

References 69

5 Credit Portfolio Correlations with Dynamic Leverage Ratios 71

5.1 Introduction 71

5.2 The Hui et al. (2007) Model 72

5.2.1 The Method of Images for Constant Coefficients 73

5.2.2 The Method of Images for Time-Varying Coefficients 74

5.3 Modelling Default Correlations in a Two-Firm Model 75

5.3.1 Default Correlations 75

5.3.2 A Two-Firm Model with Dynamic Leverage Ratios 75

5.3.3 Method of Images for Constant Coefficients at Certain Values of ρ12 78

5.3.4 Method of Images for Time-Varying Coefficients at Certain Values of ρ12 79

5.3.5 Alternative Methodologies for General Values of ρ12 81

5.4 Numerical Results 81

5.4.1 Accuracy 83

5.4.2 The Impact of Correlation between Two Firms 84

5.4.3 The Impact of Dfferent Credit Quality Paired Firms 86

5.4.4 The Impact of Volatilities 87

5.4.5 The Impact of Drift Levels 88

5.4.6 The Impact of Initial Value of Leverage Ratio Levels 89

5.4.7 Impact of Correlation between Firms and Interest Rates 89

5.4.8 The Price of Credit-Linked Notes 91

5.5 Conclusion 92

Notes 93

References 94

6 A Hierarchical Model of Tail-Dependent Asset Returns 95

6.1 Introduction 95

6.2 The Variance Compound Gamma Model 97

6.2.1 Multivariate Process for Logarithmic Asset Returns 97

6.2.2 Dependence Structure 101

6.2.3 Sampling 105

6.2.4 Copula Properties 105

6.3 An Application Example 110

6.3.1 Portfolio Setup 110

6.3.2 Test Portfolios 113

6.3.3 Parameter Setup 113

6.3.4 Simulation Results 114

6.4 Importance Sampling Algorithm 116

6.5 Conclusions 120

Appendix A: The VCG Probability Distribution Function 121

Appendix B: HAC Representation for the VCG Framework 123

Notes 124

References 124

7 Monte Carlo Methods for Portfolio Credit Risk 127

7.1 Introduction 127

7.2 Modeling Credit Portfolio Losses 128

7.2.1 Risk Measures 128

7.2.2 Modeling Dependency 129

7.3 Estimating Risk Measures via Monte Carlo 129

7.3.1 Crude Monte Carlo Estimators 130

7.3.2 Importance Sampling 131

7.4 Specific Models 133

7.4.1 The Bernoulli Mixture Model 133

7.4.2 Factor Models 135

7.4.3 Copula Models 139

7.4.4 Intensity Models 143

7.4.5 An Example Point Process Model 145

Appendix A: A Primer on Rare-event Simulation 146

7.A.1 Efficiency 147

7.A.2 Importance Sampling 147

7.A.3 The Choice of g 148

7.A.4 Adaptive Importance Sampling 149

7.A.5 Importance Sampling for Stochastic Processes 150

References 151

8 Credit Portfolio Risk and Diversification 153

8.1 Introduction 153

8.2 Model Setup 154

8.3 Independent Asset Values 155

8.4 Correlated Asset Values 159

8.5 Large Portfolio Limit 161

8.5.1 Correlated Diffusion 161

8.5.2 Correlated GARCH Process 166

8.6 Applications of the Structural Recovery Rate 168

8.7 Conclusions 169

References 169

PART III CREDIT PORTFOLIO RISK SECURITIZATION AND TRANCHING

9 Differences in Tranching Methods: Some Results and Implications 173

9.1 Introduction 173

9.2 Defining a Tranche 174

9.3 The Mathematics of Tranching 175

9.3.1 PD-based Tranching 175

9.3.2 EL-based Tranching 176

9.4 The EL of a Tranche Necessarily Increases When Either the Attachment Point or the Detachment Point is Decreased 177

9.5 Upper Bound on Tranche Expected LGD (LGDt) Assumption Given EL-based Tranches 180

9.6 “Skipping” of Some Tranches in the EL-based Approach 182

9.7 Conclusion 183

Notes 184

References 185

10 Global Structured Finance Rating 187

10.1 Introduction 187

10.2 Asset-Backed Securities 188

10.2.1 The ABS Structure for the Experiment 188

10.2.2 Cash Flow Modeling 189

10.2.3 Modeling and Simulating Defaults 192

10.2.4 Expected Loss Rating 193

10.3 Global Sensitivity Analysis 194

10.3.1 Elementary Effects 195

10.3.2 Variance-based Method 196

10.4 Global Sensitivity Analysis Results 197

10.4.1 Uncertainty Analysis 197

10.4.2 Sensitivity Analysis 198

10.5 Global Rating 202

10.5.1 Methodology 203

10.6 Conclusion 204

Acknowledgment 205

Notes 205

References 205

PART IV CREDIT DERIVATIVES

11 Analytic Dynamic Factor Copula Model 209

11.1 Introduction 209

11.2 Pricing Equations 210

11.3 One-factor Copula Model 211

11.4 Multi-period Factor Copula Models 212

11.5 Calibration 218

11.6 Numerical Examples 219

11.7 Conclusions 222

Notes 223

References 223

12 Dynamic Modeling of Credit Derivatives 225

12.1 Introduction 225

12.1.1 General Model Choice 225

12.1.2 Modeling Option Prices 226

12.1.3 Modeling Credit Risk 227

12.2 Portfolio Credit Derivatives 229

12.3 Modeling Asset Dynamics 230

12.3.1 The Market Model 230

12.3.2 The Asset-value Model 234

12.4 Empirical Analysis 236

12.4.1 Elementary Data 236

12.4.2 Implied Dividends 236

12.4.3 Market Dynamics 237

12.4.4 Asset Value Model 239

12.4.5 Tranche Pricing 240

12.4.6 Out-of-time Application 240

12.5 Conclusion 242

Notes 243

References 243

13 Pricing and Calibration in Market Models 245

13.1 Introduction 245

13.2 Basic notions 246

13.3 The model 248

13.3.1 Modeling Assumptions 248

13.3.2 Absence of Arbitrage 249

13.4 An affine specification 252

13.5 Pricing 254

13.6 Calibration 258

13.6.1 Calibration Procedure 261

13.6.2 Calibration Results 263

Appendix A: Computations 265

References 270

14 Counterparty Credit Risk and Clearing of Derivatives – From the Perspective of an Industrial Corporate with a Focus on Commodity Markets 271

14.1 Introduction 271

14.2 Credit exposures in commodity business 272

14.2.1 Settlement Exposure 272

14.2.2 Performance Exposure 273

14.2.3 Example of Fixed Price Deal with Performance Exposure 274

14.2.4 Example of a Floating Price Deal with Performance Exposure 275

14.2.5 General Remarks on Credit Exposure Concepts 276

14.3 Ex Ante exposure-reducing techniques 277

14.3.1 Payment Terms 277

14.3.2 Material Adverse Change Clauses 277

14.3.3 Master Agreements 278

14.3.4 Netting 278

14.3.5 Margining 279

14.3.6 Close Out Exposure and Threshold 280

14.4 Ex Ante risk-reducing techniques 281

14.4.1 Credit Enhancements in General 281

14.4.2 Parent Company Guarantees 281

14.4.3 Letters of Credit 282

14.4.4 Credit Insurance 283

14.4.5 Clearing via a Central Counterparty 283

14.5 Ex Post risk-reducing techniques 287

14.5.1 Factoring 287

14.5.2 Novation 287

14.5.3 Risk-reducing Trades 288

14.5.4 Hedging with CDS 288

14.5.5 Hedging with Contingent-CDS 290

14.5.6 Hedging with Puts on Equity 290

14.6 Ex Post work out considerations 290

14.7 Practical credit risk management and pricing 291

14.8 Peculiarities of commodity markets 292

14.9 Peculiarities of commodity related credit portfolios 294

14.10 Credit Risk Capital for a commodity related portfolio – measured with an extension of CreditMetrics 295

14.11 Case study: CreditRisk+ applied to a commodity related credit portfolio 300

14.12 Outlook 302

Notes 303

References 304

15 CDS Industrial Sector Indices, Credit and Liquidity Risk 307

15.1 Introduction 307

15.2 The Data 308

15.3 Methodology and Results 312

15.3.1 Preliminary Analysis 312

15.3.2 Common Factor Analysis 316

15.4 Stability of Relations 321

15.5 Conclusions 322

References 323

16 Risk Transfer and Pricing of Illiquid Assets with Loan CDS 325

16.1 Introduction 325

16.2 Shipping Market 326

16.3 Loan Credit Default Swaps 327

16.3.1 LCDS Pricing 327

16.3.2 Modeling LCDS Under the Intensity-based Model 329

16.4 Valuation Framework for LCDS 331

16.4.1 The Structural Approach 331

16.4.2 Credit Risk in Shipping Loans 332

16.4.3 Valuation of LCDS on Shipping Loans 334

16.4.4 Simulation Model 335

16.5 Numerical Results 336

16.6 Conclusion 338

Appendix A: Monte Carlo Parameterization 339

References 339

PART V REGULATION

17 Regulatory Capital Requirements for Securitizations 343

17.1 Regulatory Approaches for Securitizations 343

17.1.1 Ratings Based Approach (RBA) 343

17.1.2 Supervisory Formula Approach (SFA) 346

17.1.3 Standardized Approach (SA) 353

17.2 Post-crisis Revisions to the Basel Framework 353

17.3 Outlook 354

Notes 355

References 355

18 Regulating OTC Derivatives 357

18.1 Overview 357

18.2 The Wall Street Transparency and Accountability Part of the Dodd–Frank Act of 2010 358

18.2.1 Which Derivatives Will Be Affected? 359

18.2.2 Clearing 359

18.2.3 Transparency and Reporting Requirements 361

18.2.4 Bankruptcy-Related Issues 361

18.2.5 Trading and Risk Mitigation 362

18.2.6 Extraterritorial Enforcement and International Coordination 363

18.3 Evaluation of Proposed Reforms 364

18.4 Clearing, Margins, Transparency, and Systemic Risk of Clearinghouses 369

18.4.1 Migration to Centralized Clearing Should Start with Credit Derivatives 369

18.4.2 Margin Requirements versus Transparency 370

18.4.3 Toward a Transparency Standard 374

18.4.4 Deal with the Dealers First 375

18.4.5 Proposed Reforms Will Help End Users 377

18.4.6 Centralized Clearinghouses: Too Systemic to Fail? 380

18.5 Conclusion: How Will the Derivatives Reforms Affect Global Finance in Future? 383

Appendix A: Items Concerning OTC Derivatives Left by the Dodd–Frank Act for Future Study 385

Appendix B: Current OTC Disclosure Provided by Dealer Banks 387

Appendix C: Sovereign Credit Default Swaps Markets 392

Notes 398

References 401

19 Governing Derivatives after the Financial Crisis: The Devil is in the Details 403

19.1 Introduction 403

19.2 Securitization and Risk Management 404

19.2.1 Securitization and Interest Rate Risk 405

19.2.2 Securitization and Credit Risk 405

19.2.3 Securitization and Credit Risk Transfer 406

19.2.4 Skin in the Game 407

19.3 The Regulation of Derivative Contracts 407

19.3.1 Regulation Prior to 2000 407

19.3.2 The Commodity Futures Modernization Act (CFMA) of 2000 408

19.3.3 The Dodd–Frank Wall Street Reform and Consumer Protection Act of 2010 408

19.4 Regulatory Challenges and Responses 409

19.4.1 Fostering an Exchange-traded Credit Derivatives Market 409

19.4.2 Counterparty Risk 410

19.4.3 Disclosure and Transparency 411

19.4.4 Accounting, Valuation and Stability Issues 412

19.5 Conclusions 412

Notes 413

References 415

About the Authors 417

Index 429

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Daniel Röschis Professor of Finance and Head of the Institute of Banking and Finance at Leibniz Universität Hannover. He received a PhD from the University of Regensburg. His work covers a broad range in Banking, Asset Pricing and Empirical Finance. He has published numerous articles on Risk Management, Credit Risk, Banking, Quantitative Finance and Financial Econometrics in leading international journals. He has been conducting research projects with supervising authorities and is consulting financial institutions on risk management issues.

Harald Scheule is Associate Professor of Finance at the University of Technology, Sydney. His expertise is in the area of banking, Financial Risk Measurement and Management, Insurance, Prudential Regulation, Securities Evaluation and Structured Finance. He is a regional director of the Global Association of Risk Professionals. His research work has been accepted for publication in a wide range of journals including the European Financial Management, International Review of Finance, Journal of Banking and Finance, Journal of Financial Research, Journal of the Operational Research Society and The European Journal of Finance. He has worked with prudential regulators of financial institutions and undertaken consulting work for a wide range of financial institutions and service providers in Australia, Europe and North America.
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