{"product_id":"credit-derivatives-pricing-models-isbn-9780470842911","title":"Credit Derivatives Pricing Models","description":"The credit derivatives market is booming and, for the first time, expanding into the banking sector which previously has had very little exposure to quantitative modeling. This phenomenon has forced a large number of professionals to confront this issue for the first time. \u003ci\u003eCredit Derivatives Pricing Models\u003c\/i\u003e provides an extremely comprehensive overview of the most current areas in credit risk modeling as applied to the pricing of credit derivatives. As one of the first books to uniquely focus on pricing, this title is also an excellent complement to other books on the application of credit derivatives. Based on proven techniques that have been tested time and again, this comprehensive resource provides readers with the knowledge and guidance to effectively use credit derivatives pricing models. Filled with relevant examples that are applied to real-world pricing problems, \u003ci\u003eCredit Derivatives Pricing Models\u003c\/i\u003e paves a clear path for a better understanding of this complex issue.  \u003cp\u003e\u003cbr\u003e Dr. Philipp J. Schönbucher is a professor at the Swiss Federal Institute of Technology (ETH), Zurich, and has degrees in mathematics from Oxford University and a PhD in economics from Bonn University. He has taught various training courses organized by ICM and CIFT, and lectured at risk conferences for practitioners on credit derivatives pricing, credit risk modeling, and implementation.\u003c\/p\u003eDer Markt für Kreditderivate boomt und dehnt sich erstmals auch auf den Bankensektor aus, wo man bislang nur wenig mit quantitativen Modellen zu tun hatte.\u003cbr\u003e \u003cbr\u003e \"Credit Derivatives Pricing Models\" ist das erste Buch auf dem Markt, das einen topaktuellen und umfassenden Überblick über die neuesten Preisbildungsmodelle bei Kreditderivaten gibt.\u003cbr\u003e \u003cbr\u003e Mit einer Fülle von Beispielen, die sich auf Probleme bei der Preisbildung in der Praxis beziehen.\u003cbr\u003e \u003cbr\u003e Verständlich und nachvollziehbar geschrieben.\u003cbr\u003e \u003cbr\u003e Praxis- und anwendungsorientiert: Die hier beschriebenen Anwendungen basieren auf Preisbildungsproblemen, wie sie in der täglichen Praxis vorkommen.\u003cbr\u003e \u003cbr\u003e \"Credit Derivatives Pricing Models\" konzentriert sich vornehmlich auf die Kreditrisikomodellierung im Zusammenhang mit der Preisbildung bei Kreditderivaten. Deshalb bietet sich dieser Band auch hervorragend als Ergänzungsliteratur an zu Titeln, die die Anwendungsbereiche von Kreditderivaten behandeln. \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAcknowledgements xv\u003c\/p\u003e \u003cp\u003eAbbreviations xvii\u003c\/p\u003e \u003cp\u003eNotation xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The world of credit risk 1\u003c\/p\u003e \u003cp\u003e1.2 The components of credit risk 2\u003c\/p\u003e \u003cp\u003e1.3 Market structure 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Credit Derivatives: Overview and Hedge-Based Pricing 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 The emergence of a new class of derivatives 7\u003c\/p\u003e \u003cp\u003e2.2 Terminology 7\u003c\/p\u003e \u003cp\u003e2.3 Underlying assets 10\u003c\/p\u003e \u003cp\u003e2.3.1 Loans 10\u003c\/p\u003e \u003cp\u003e2.3.2 Bonds 11\u003c\/p\u003e \u003cp\u003e2.3.3 Convertible bonds 12\u003c\/p\u003e \u003cp\u003e2.3.4 Counterparty risk 12\u003c\/p\u003e \u003cp\u003e2.4 Asset swaps 12\u003c\/p\u003e \u003cp\u003e2.5 Total return swaps 13\u003c\/p\u003e \u003cp\u003e2.6 Credit default swaps 15\u003c\/p\u003e \u003cp\u003e2.7 Hedge-based pricing 19\u003c\/p\u003e \u003cp\u003e2.7.1 Hedge instruments 20\u003c\/p\u003e \u003cp\u003e2.7.2 Short positions in defaultable bonds 20\u003c\/p\u003e \u003cp\u003e2.7.3 Asset swap packages 22\u003c\/p\u003e \u003cp\u003e2.7.4 Total return swaps 25\u003c\/p\u003e \u003cp\u003e2.7.5 Credit default swaps 27\u003c\/p\u003e \u003cp\u003e2.8 Exotic credit derivatives 37\u003c\/p\u003e \u003cp\u003e2.8.1 Default digital swaps 37\u003c\/p\u003e \u003cp\u003e2.8.2 Exotic default payments in credit default swaps 38\u003c\/p\u003e \u003cp\u003e2.8.3 Rating-triggered credit default swaps 39\u003c\/p\u003e \u003cp\u003e2.8.4 Options on defaultable bonds 40\u003c\/p\u003e \u003cp\u003e2.8.5 Credit spread options 41\u003c\/p\u003e \u003cp\u003e2.9 Default correlation products and CDOs 43\u003c\/p\u003e \u003cp\u003e2.9.1 First-to-default swaps and basket default swaps 43\u003c\/p\u003e \u003cp\u003e2.9.2 First loss layers 44\u003c\/p\u003e \u003cp\u003e2.9.3 Collateralised debt obligations 46\u003c\/p\u003e \u003cp\u003e2.10 Credit-linked notes 49\u003c\/p\u003e \u003cp\u003e2.11 Guide to the literature 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Credit Spreads and Bond Price-Based Pricing 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Credit spreads and implied default probabilities 52\u003c\/p\u003e \u003cp\u003e3.1.1 Risk-neutral probabilities 52\u003c\/p\u003e \u003cp\u003e3.1.2 Setup 52\u003c\/p\u003e \u003cp\u003e3.1.3 The fundamental relationship 54\u003c\/p\u003e \u003cp\u003e3.1.4 The implied survival probability 54\u003c\/p\u003e \u003cp\u003e3.1.5 Conditional survival probabilities and implied hazard rates 56\u003c\/p\u003e \u003cp\u003e3.1.6 Relation to forward spreads 58\u003c\/p\u003e \u003cp\u003e3.2 Recovery modelling 60\u003c\/p\u003e \u003cp\u003e3.3 Building blocks for credit derivatives pricing 61\u003c\/p\u003e \u003cp\u003e3.4 Pricing with the building blocks 64\u003c\/p\u003e \u003cp\u003e3.4.1 Defaultable fixed-coupon bond 64\u003c\/p\u003e \u003cp\u003e3.4.2 Defaultable floater 65\u003c\/p\u003e \u003cp\u003e3.4.3 Variants of coupon bonds 66\u003c\/p\u003e \u003cp\u003e3.4.4 Credit default swaps 66\u003c\/p\u003e \u003cp\u003e3.4.5 Forward start CDSs 68\u003c\/p\u003e \u003cp\u003e3.4.6 Default digital swaps 68\u003c\/p\u003e \u003cp\u003e3.4.7 Asset swap packages 69\u003c\/p\u003e \u003cp\u003e3.5 Constructing and calibrating credit spread curves 69\u003c\/p\u003e \u003cp\u003e3.5.1 Parametric forms for the spread curves 70\u003c\/p\u003e \u003cp\u003e3.5.2 Semi-parametric and non-parametric calibration 72\u003c\/p\u003e \u003cp\u003e3.5.3 Approximative and aggregate fits 74\u003c\/p\u003e \u003cp\u003e3.5.4 Calibration example 75\u003c\/p\u003e \u003cp\u003e3.6 Spread curves: issues in implementation 77\u003c\/p\u003e \u003cp\u003e3.6.1 Which default-free interest rates should one use? 77\u003c\/p\u003e \u003cp\u003e3.6.2 Recovery uncertainty 79\u003c\/p\u003e \u003cp\u003e3.6.3 Bucket hedging 81\u003c\/p\u003e \u003cp\u003e3.7 Spread curves: discussion 82\u003c\/p\u003e \u003cp\u003e3.8 Guide to the literature 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Mathematical Background 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Stopping times 86\u003c\/p\u003e \u003cp\u003e4.2 The hazard rate 87\u003c\/p\u003e \u003cp\u003e4.3 Point processes 88\u003c\/p\u003e \u003cp\u003e4.4 The intensity 88\u003c\/p\u003e \u003cp\u003e4.5 Marked point processes and the jump measure 91\u003c\/p\u003e \u003cp\u003e4.6 The compensator measure 93\u003c\/p\u003e \u003cp\u003e4.6.1 Random measures in discrete time 95\u003c\/p\u003e \u003cp\u003e4.7 Examples for compensator measures 97\u003c\/p\u003e \u003cp\u003e4.8 Itô’s lemma for jump processes 100\u003c\/p\u003e \u003cp\u003e4.9 Applications of Itô’s lemma 101\u003c\/p\u003e \u003cp\u003e4.9.1 Predictable compensators for jump processes 102\u003c\/p\u003e \u003cp\u003e4.9.2 Itô product rule and Itô quotient rule 103\u003c\/p\u003e \u003cp\u003e4.9.3 The stochastic exponential 104\u003c\/p\u003e \u003cp\u003e4.10 Martingale measure, fundamental pricing rule and incompleteness 105\u003c\/p\u003e \u003cp\u003e4.11 Change of numeraire and pricing measure 107\u003c\/p\u003e \u003cp\u003e4.11.1 The Radon–Nikodym theorem 107\u003c\/p\u003e \u003cp\u003e4.11.2 The Girsanov theorem 108\u003c\/p\u003e \u003cp\u003e4.12 The change of measure\/change of numeraire technique 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Advanced Credit Spread Models 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Poisson processes 111\u003c\/p\u003e \u003cp\u003e5.1.1 A model for default arrival risk 111\u003c\/p\u003e \u003cp\u003e5.1.2 Intuitive construction of a Poisson process 112\u003c\/p\u003e \u003cp\u003e5.1.3 Properties of Poisson processes 113\u003c\/p\u003e \u003cp\u003e5.1.4 Spreads with Poisson processes 115\u003c\/p\u003e \u003cp\u003e5.2 Inhomogeneous Poisson processes 115\u003c\/p\u003e \u003cp\u003e5.2.1 Pricing the building blocks 117\u003c\/p\u003e \u003cp\u003e5.3 Stochastic credit spreads 118\u003c\/p\u003e \u003cp\u003e5.3.1 Cox processes 119\u003c\/p\u003e \u003cp\u003e5.3.2 Pricing the building blocks 125\u003c\/p\u003e \u003cp\u003e5.3.3 General point processes 126\u003c\/p\u003e \u003cp\u003e5.3.4 Compound Poisson processes 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Recovery Modelling 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Presentation of the different recovery models 132\u003c\/p\u003e \u003cp\u003e6.1.1 Zero recovery 132\u003c\/p\u003e \u003cp\u003e6.1.2 Recovery of treasury 133\u003c\/p\u003e \u003cp\u003e6.1.3 Multiple defaults and recovery of market value 135\u003c\/p\u003e \u003cp\u003e6.1.4 Recovery of par 141\u003c\/p\u003e \u003cp\u003e6.1.5 Stochastic recovery and recovery risk 143\u003c\/p\u003e \u003cp\u003e6.1.6 Common parametric distribution functions for recoveries 147\u003c\/p\u003e \u003cp\u003e6.1.7 Valuation of the delivery option in a CDS 148\u003c\/p\u003e \u003cp\u003e6.2 Comparing the recovery models 150\u003c\/p\u003e \u003cp\u003e6.2.1 Theoretical comparison of the recovery models 150\u003c\/p\u003e \u003cp\u003e6.2.2 Empirical analysis of recovery rates 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Implementation of Intensity-Based Models 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Tractable models of the spot intensity 166\u003c\/p\u003e \u003cp\u003e7.1.1 The two-factor Gaussian model 167\u003c\/p\u003e \u003cp\u003e7.1.2 The multifactor Gaussian model 171\u003c\/p\u003e \u003cp\u003e7.1.3 Implied survival probabilities 172\u003c\/p\u003e \u003cp\u003e7.1.4 Payoffs at default 174\u003c\/p\u003e \u003cp\u003e7.2 The multifactor CIR model 174\u003c\/p\u003e \u003cp\u003e7.2.1 Bond prices 175\u003c\/p\u003e \u003cp\u003e7.2.2 Affine combinations of independent non-central chi-squared distributed random variables 176\u003c\/p\u003e \u003cp\u003e7.2.3 Factor distributions 178\u003c\/p\u003e \u003cp\u003e7.3 Credit derivatives in the CIR model 179\u003c\/p\u003e \u003cp\u003e7.3.1 Default digital payoffs 180\u003c\/p\u003e \u003cp\u003e7.3.2 Calculations to the Gaussian model 180\u003c\/p\u003e \u003cp\u003e7.3.3 Calculations to the CIR model 184\u003c\/p\u003e \u003cp\u003e7.4 Tree models 187\u003c\/p\u003e \u003cp\u003e7.4.1 The tree implementation: inputs 187\u003c\/p\u003e \u003cp\u003e7.4.2 Default branching 188\u003c\/p\u003e \u003cp\u003e7.4.3 The implementation steps 190\u003c\/p\u003e \u003cp\u003e7.4.4 Building trees: the Hull–White algorithm 190\u003c\/p\u003e \u003cp\u003e7.4.5 Fitting the tree: default-free interest rates 193\u003c\/p\u003e \u003cp\u003e7.4.6 Combining the trees 194\u003c\/p\u003e \u003cp\u003e7.4.7 Fitting the combined tree 197\u003c\/p\u003e \u003cp\u003e7.4.8 Applying the tree 198\u003c\/p\u003e \u003cp\u003e7.4.9 Extensions and conclusion 199\u003c\/p\u003e \u003cp\u003e7.5 PDE-Based implementation 200\u003c\/p\u003e \u003cp\u003e7.6 Modelling term structures of credit spreads 204\u003c\/p\u003e \u003cp\u003e7.6.1 Intensity models in a Heath, Jarrow, Morton framework 206\u003c\/p\u003e \u003cp\u003e7.7 Monte Carlo simulation 211\u003c\/p\u003e \u003cp\u003e7.7.1 Pathwise simulation of diffusion processes 214\u003c\/p\u003e \u003cp\u003e7.7.2 Simulation of recovery rates 219\u003c\/p\u003e \u003cp\u003e7.8 Guide to the literature 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Credit Rating Models 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 223\u003c\/p\u003e \u003cp\u003e8.1.1 Empirical observations 224\u003c\/p\u003e \u003cp\u003e8.1.2 An example 225\u003c\/p\u003e \u003cp\u003e8.2 The rating process and transition probabilities 226\u003c\/p\u003e \u003cp\u003e8.2.1 Discrete-time Markov chains 229\u003c\/p\u003e \u003cp\u003e8.2.2 Continuous-time Markov chains 229\u003c\/p\u003e \u003cp\u003e8.2.3 Connection to Poisson processes 231\u003c\/p\u003e \u003cp\u003e8.3 Estimation of transition intensities 233\u003c\/p\u003e \u003cp\u003e8.3.1 The cohort method 233\u003c\/p\u003e \u003cp\u003e8.3.2 The embedding problem: finding a generator matrix 234\u003c\/p\u003e \u003cp\u003e8.4 Direct estimation of transition intensities 238\u003c\/p\u003e \u003cp\u003e8.5 Pricing with deterministic generator matrix 239\u003c\/p\u003e \u003cp\u003e8.5.1 Pricing zero-coupon bonds 239\u003c\/p\u003e \u003cp\u003e8.5.2 Pricing derivatives on the credit rating 240\u003c\/p\u003e \u003cp\u003e8.5.3 General payoffs 241\u003c\/p\u003e \u003cp\u003e8.5.4 Rating trees 242\u003c\/p\u003e \u003cp\u003e8.5.5 Downgrade triggers 243\u003c\/p\u003e \u003cp\u003e8.5.6 Hedging rating transitions 245\u003c\/p\u003e \u003cp\u003e8.6 The calibration of rating transition models 246\u003c\/p\u003e \u003cp\u003e8.6.1 Deterministic intensity approaches 246\u003c\/p\u003e \u003cp\u003e8.6.2 Incorporating rating momentum 249\u003c\/p\u003e \u003cp\u003e8.6.3 Stochastic rating transition intensities 250\u003c\/p\u003e \u003cp\u003e8.7 A general HJM framework 251\u003c\/p\u003e \u003cp\u003e8.8 Conclusion 253\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Firm Value and Share Price-Based Models 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 The approach 255\u003c\/p\u003e \u003cp\u003e9.1.1 Modelling philosophy 255\u003c\/p\u003e \u003cp\u003e9.1.2 An example 256\u003c\/p\u003e \u003cp\u003e9.1.3 State variables and modelling 259\u003c\/p\u003e \u003cp\u003e9.1.4 The time of default 261\u003c\/p\u003e \u003cp\u003e9.2 Pricing equations 263\u003c\/p\u003e \u003cp\u003e9.2.1 The firm’s value model 263\u003c\/p\u003e \u003cp\u003e9.2.2 The pricing equation 264\u003c\/p\u003e \u003cp\u003e9.2.3 Some other securities 265\u003c\/p\u003e \u003cp\u003e9.2.4 Hedging 268\u003c\/p\u003e \u003cp\u003e9.3 Solutions to the pricing equation 269\u003c\/p\u003e \u003cp\u003e9.3.1 The T-forward measure 269\u003c\/p\u003e \u003cp\u003e9.3.2 Time change 270\u003c\/p\u003e \u003cp\u003e9.3.3 The hitting probability 270\u003c\/p\u003e \u003cp\u003e9.3.4 Putting it together 271\u003c\/p\u003e \u003cp\u003e9.3.5 The Longstaff–Schwartz results 271\u003c\/p\u003e \u003cp\u003e9.3.6 Strategic default 273\u003c\/p\u003e \u003cp\u003e9.4 A practical implementation: KMV 275\u003c\/p\u003e \u003cp\u003e9.4.1 The default point 275\u003c\/p\u003e \u003cp\u003e9.4.2 The time horizon 275\u003c\/p\u003e \u003cp\u003e9.4.3 The initial value of the firm’s assets and its volatility 275\u003c\/p\u003e \u003cp\u003e9.4.4 The distance to default 276\u003c\/p\u003e \u003cp\u003e9.5 Unobservable firm’s values and CreditGrades 277\u003c\/p\u003e \u003cp\u003e9.5.1 A simple special case: delayed observation 280\u003c\/p\u003e \u003cp\u003e9.5.2 The idea of Lardy and Finkelstein: CreditGrades and E2C 281\u003c\/p\u003e \u003cp\u003e9.6 Advantages and disadvantages 284\u003c\/p\u003e \u003cp\u003e9.6.1 Empirical evidence 284\u003c\/p\u003e \u003cp\u003e9.6.2 Discussion 286\u003c\/p\u003e \u003cp\u003e9.7 Guide to the literature 286\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Models for Default Correlation 289\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Default correlation basics 290\u003c\/p\u003e \u003cp\u003e10.1.1 Empirical evidence 290\u003c\/p\u003e \u003cp\u003e10.1.2 Terminology 291\u003c\/p\u003e \u003cp\u003e10.1.3 Linear default correlation, conditional default probabilities, joint default probabilities 292\u003c\/p\u003e \u003cp\u003e10.1.4 The size of the impact of default correlation 293\u003c\/p\u003e \u003cp\u003e10.1.5 Price bounds for FtD swaps 293\u003c\/p\u003e \u003cp\u003e10.1.6 The need for theoretical models of default correlations 297\u003c\/p\u003e \u003cp\u003e10.2 Independent defaults 298\u003c\/p\u003e \u003cp\u003e10.2.1 The binomial distribution function 298\u003c\/p\u003e \u003cp\u003e10.2.2 Properties of the binomial distribution function 299\u003c\/p\u003e \u003cp\u003e10.2.3 The other extreme: perfectly dependent defaults 300\u003c\/p\u003e \u003cp\u003e10.3 The binomial expansion method 301\u003c\/p\u003e \u003cp\u003e10.4 Factor models 305\u003c\/p\u003e \u003cp\u003e10.4.1 One-factor dependence of defaults 305\u003c\/p\u003e \u003cp\u003e10.4.2 A simplified firm’s value model 305\u003c\/p\u003e \u003cp\u003e10.4.3 The distribution of the defaults 307\u003c\/p\u003e \u003cp\u003e10.4.4 The large portfolio approximation 309\u003c\/p\u003e \u003cp\u003e10.4.5 Generalisations 312\u003c\/p\u003e \u003cp\u003e10.4.6 Portfolios of two asset classes 313\u003c\/p\u003e \u003cp\u003e10.4.7 Some remarks on implementation 314\u003c\/p\u003e \u003cp\u003e10.5 Correlated defaults in intensity models 315\u003c\/p\u003e \u003cp\u003e10.5.1 The intensity of the default counting process 315\u003c\/p\u003e \u003cp\u003e10.5.2 Correlated intensities 316\u003c\/p\u003e \u003cp\u003e10.5.3 Stress events in intensity models 318\u003c\/p\u003e \u003cp\u003e10.5.4 Default contagion\/infectious defaults 321\u003c\/p\u003e \u003cp\u003e10.6 Correlated defaults in firm’s value models 321\u003c\/p\u003e \u003cp\u003e10.7 Copula functions and dependency concepts 326\u003c\/p\u003e \u003cp\u003e10.7.1 Copula functions 327\u003c\/p\u003e \u003cp\u003e10.7.2 Examples of copulae 330\u003c\/p\u003e \u003cp\u003e10.7.3 Archimedean copulae 333\u003c\/p\u003e \u003cp\u003e10.8 Default modelling with copula functions 337\u003c\/p\u003e \u003cp\u003e10.8.1 Static copula models for default correlation 337\u003c\/p\u003e \u003cp\u003e10.8.2 Large portfolio loss distributions for Archimedean copulae 340\u003c\/p\u003e \u003cp\u003e10.8.3 A semi-dynamic copula model 343\u003c\/p\u003e \u003cp\u003e10.8.4 Dynamic copula-dependent defaults 349\u003c\/p\u003e \u003cp\u003eBibliography 361\u003c\/p\u003e \u003cp\u003eIndex 369\u003c\/p\u003e  \u003cb\u003ePHILIPP J. SCHÖNBUCHER\u003c\/b\u003e is Assistant Professor for Risk Management in the Mathematics Department at ETH Zurich. He has been an active researcher in the areas of credit risk modelling and credit derivatives pricing for the past seven years. His contributions include models for the term structure of credit spreads and the dynamic copula-approach for portfolio credit risk. Through his activities in training and consulting on credit derivatives he has gained valuable insights into the usability, strengths and weaknesses of the different credit derivatives pricing models in a practical context.  \u003cp\u003eDr. Schönbucher holds a M.Sc. in mathematics from Oxford University, and diploma and a Ph.D in economics from Bonn University.\u003cbr\u003e \u003c\/p\u003e  In this book, Philipp Schönbucher covers all the important modelling approaches from hedge-based pricing to stochastic-intensity models, credit rating models and firm's value based models, concluding with a large chapter on portfolio credit risk models. The author builds the models starting from simple basic models, introducing complexity only where it is needed, and explaining implementation, data collection and calibration on the way. The advantages and disadvantages of the different pricing approaches are clearly confronted, and the effects of hidden assumptions on the output of the models are identified.  \u003cp\u003eThe book is an indispensable tool for credit derivatives traders, quantitative analysts, software developers, risk managers, regulators, auditors, and anybody interested in how credit derivatives are priced.\u003cbr\u003e \u003c\/p\u003e  Since its inception, the market for credit derivatives has shown impressive growth and is expected to hit a volume of more than $4.8 trillion by 2004. Credit derivatives have begun to transform modern banking; they have become a standard instrument for the management of default risk; they are being used for risk management and hedging as well as for speculation, balance-sheet management and regulatory capital purposes.  \u003cp\u003eDespite their great usefulness, even established professionals often feel insecure when it comes to the quantitative analysis of the prices and risks of credit derivatives. Confronted with a bewildering variety of fundamentally different pricing approaches, it can be very challenging to understand their relative advantages and disadvantages and to choose the \"correct\" one for the problem at hand.\u003c\/p\u003e \u003cp\u003eIn this book, the author carefully explains the different pricing models for credit derivatives in a very application-oriented way. Based on his wide experience in professional training for credit derivatives analysis, the models are developed with a view to their application to real pricing problems rather than just presenting the theory.\u003c\/p\u003e \u003cp\u003ePhilipp Schönbucher is one of the most talented researchers of his generation. He has taken the Credit Derivatives world by storm. In this book he carefully explains the concepts and the mathematics behind all of the most important and popular credit risk models. Professor Schönbucher has filled an important gap on the quantitative finance bookshelf. –Paul Wilmott\u003c\/p\u003e \u003cp\u003eThe reader is presented with a clear, concise and readable treatment of credit pricing models that will appeal to practitioners and academics. It provides a useful roadmap to the many daily challenges that face practitioners. It will become a standard reference.\u003cbr\u003e –Stuart M. Turnbull, Senior Vice President, Fixed Income Research, Lehman Brothers, NY\u003c\/p\u003e \u003cp\u003e\"This is the most comprehensive, and also the clearest, book on the details of constructing credit risk models that I have read. Throughout, it is directly useful for general value-at-risk credit modelling as well as its stated focus of credit derivatives. Readability is greatly enhanced by its step-by-step organization across what has grown to be a large topic area and the focus of its single author, as opposed to a collection of disjointed papers. Alternative modelling frameworks are written in a common notation and the reader is given all the details needed for direct implementation. The author, Philipp Schönbucher, is clearly one of the top researchers in this area, even before the writing of this book.\" –Greg M Gupton, DefaultRisk.com\u003c\/p\u003e \u003cp\u003e\"Philipp addresses a wide range of modelling issues in the fast growing market of credit derivatives. He covers a broad spectrum of topics starting with the simple everyday trading tools while gradually building up to the more complex mathematical models. It successfully bridges the gap between academia and practice in an elegant and easy style, making it a valuable book for a wide audience\"  –Ebbe Rogge, Product Development Group, Financial Markets, ABN AMRO\u003cbr\u003e \u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989003583717,"sku":"NP9780470842911","price":179.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470842911.jpg?v=1761782393","url":"https:\/\/k12savings.com\/es\/products\/credit-derivatives-pricing-models-isbn-9780470842911","provider":"K12savings","version":"1.0","type":"link"}