{"product_id":"active-credit-portfolio-management-in-practice-isbn-9780470080184","title":"Active Credit Portfolio Management in Practice","description":"State-of-the-art techniques and tools needed to facilitate effective credit portfolio management and robust quantitative credit analysis  \u003cp\u003eFilled with in-depth insights and expert advice, \u003ci\u003eActive Credit Portfolio Management in Practice\u003c\/i\u003e serves as a comprehensive introduction to both the theory and real-world practice of credit portfolio management. The authors have written a text that is technical enough both in terms of background and implementation to cover what practitioners and researchers need for actually applying these types of risk management tools in large organizations but which at the same time, avoids technical proofs in favor of real applications.  Throughout this book, readers will be introduced to the theoretical foundations of this discipline, and learn about structural, reduced-form, and econometric models successfully used in the market today. The book is full of hands-on examples and anecdotes. Theory is illustrated with practical application. The authors' Website provides additional software tools in the form of Excel spreadsheets, Matlab code and S-Plus code. Each section of the book concludes with review questions designed to spark further discussion and reflection on the concepts presented.\u003c\/p\u003e \u003cp\u003eForeword xi\u003c\/p\u003e \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 The Framework: Definitions and Concepts 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Credit? 2\u003c\/p\u003e \u003cp\u003eEvolution of Credit Markets 7\u003c\/p\u003e \u003cp\u003eDefining Risk 11\u003c\/p\u003e \u003cp\u003eA Word about Regulation 13\u003c\/p\u003e \u003cp\u003eWhat Are Credit Models Good For? 14\u003c\/p\u003e \u003cp\u003eActive Credit Portfolio Management (ACPM) 16\u003c\/p\u003e \u003cp\u003eFramework at 30,000 Feet 19\u003c\/p\u003e \u003cp\u003eBuilding Blocks of Portfolio Risk 23\u003c\/p\u003e \u003cp\u003eUsing PDs in Practice 32\u003c\/p\u003e \u003cp\u003eValue, Price, and Spread 34\u003c\/p\u003e \u003cp\u003eDefining Default 38\u003c\/p\u003e \u003cp\u003ePortfolio Performance Metrics 38\u003c\/p\u003e \u003cp\u003eData and Data Systems 42\u003c\/p\u003e \u003cp\u003eReview Questions 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 ACPM in Practice 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBank Valuation 50\u003c\/p\u003e \u003cp\u003eOrganizing Financial Institutions: Dividing into Two Business Lines 52\u003c\/p\u003e \u003cp\u003eEmphasis on Credit Risk 57\u003c\/p\u003e \u003cp\u003eMarket Trends Supporting ACPM 59\u003c\/p\u003e \u003cp\u003eFinancial Instruments Used for Hedging and Managing Risk in a Credit Portfolio 60\u003c\/p\u003e \u003cp\u003eMark-to-Market and Transfer Pricing 63\u003c\/p\u003e \u003cp\u003eMetrics for Managing a Credit Portfolio 68\u003c\/p\u003e \u003cp\u003eData and Models 72\u003c\/p\u003e \u003cp\u003eEvaluating an ACPM Unit 75\u003c\/p\u003e \u003cp\u003eManaging a Research Team 77\u003c\/p\u003e \u003cp\u003eConclusion 86\u003c\/p\u003e \u003cp\u003eReview Questions 87\u003c\/p\u003e \u003cp\u003eExercises 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Structural Models 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStructural Models in Context 91\u003c\/p\u003e \u003cp\u003eA Basic Structural Model 95\u003c\/p\u003e \u003cp\u003eBlack-Scholes-Merton 100\u003c\/p\u003e \u003cp\u003eValuation 107\u003c\/p\u003e \u003cp\u003eModifying BSM 117\u003c\/p\u003e \u003cp\u003eFirst Passage Time: Black-Cox 118\u003c\/p\u003e \u003cp\u003ePractical Implementation: Vasicek-Kealhofer 124\u003c\/p\u003e \u003cp\u003eStochastic Interest Rates: Longstaff-Schwartz 145\u003c\/p\u003e \u003cp\u003eJump-Diffusion Models: Zhou 150\u003c\/p\u003e \u003cp\u003eEndogenous Default Barrier (Taxes and Bankruptcy Costs): Leland-Toft 151\u003c\/p\u003e \u003cp\u003eCorporate Transaction Analysis 156\u003c\/p\u003e \u003cp\u003eLiquidity 159\u003c\/p\u003e \u003cp\u003eOther Structural Approaches 161\u003c\/p\u003e \u003cp\u003eConclusion 171\u003c\/p\u003e \u003cp\u003eAppendix 3A: Derivation of Black-Scholes-Merton Framework for Calculating Distance to Default (DD) 171\u003c\/p\u003e \u003cp\u003eAppendix 3B: Derivation of Conversion of Physical Probability of Default (PD) to a Risk-Neutral Probability of Default (PD Q) 177\u003c\/p\u003e \u003cp\u003eReview Questions 179\u003c\/p\u003e \u003cp\u003eExercises 179\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Econometric Models 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDiscrete-Choice Models 186\u003c\/p\u003e \u003cp\u003eEarly Discrete-Choice Models: Beaver (1966) and Altman (1968) 191\u003c\/p\u003e \u003cp\u003eHazard Rate (Duration) Models 196\u003c\/p\u003e \u003cp\u003eExample of a Hazard-Rate Framework for Predicting Default: Shumway (2001) 204\u003c\/p\u003e \u003cp\u003eHazard Rates versus Discrete Choice 206\u003c\/p\u003e \u003cp\u003ePractical Applications: Falkenstein et al. (2000) and Dwyer and Stein (2004) 207\u003c\/p\u003e \u003cp\u003eCalibrating Econometric Models 215\u003c\/p\u003e \u003cp\u003eCalibrating to PDs 216\u003c\/p\u003e \u003cp\u003eCalibrating to Ratings 227\u003c\/p\u003e \u003cp\u003eInterpreting the Relative Influence of Factors in Econometric Models 234\u003c\/p\u003e \u003cp\u003eData Issues 238\u003c\/p\u003e \u003cp\u003eTaxonomy of Data Woes 241\u003c\/p\u003e \u003cp\u003eBiased Samples Cannot Easily Be Fixed 244\u003c\/p\u003e \u003cp\u003eConclusion 249\u003c\/p\u003e \u003cp\u003eAppendix 4A: Some Alternative Default Model Specifications 249\u003c\/p\u003e \u003cp\u003eReview Questions 252\u003c\/p\u003e \u003cp\u003eExercises 252\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Loss Given Default 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRoad to Recovery: The Timeline of Default Resolution 258\u003c\/p\u003e \u003cp\u003eMeasures of LGD (Recovery) 260\u003c\/p\u003e \u003cp\u003eThe Relationship between Market Prices and Ultimate Recovery 265\u003c\/p\u003e \u003cp\u003eApproaches to Modeling LGD: The LossCalc (2002, 2005) Approaches and Extensions 273\u003c\/p\u003e \u003cp\u003eConclusion 285\u003c\/p\u003e \u003cp\u003eReview Questions 286\u003c\/p\u003e \u003cp\u003eExercises 286\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Reduced-Form Models 289\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReduced-Form Models in Context 291\u003c\/p\u003e \u003cp\u003eBasic Intensity Models 296\u003c\/p\u003e \u003cp\u003eA Brief Interlude to Discuss Valuation 310\u003c\/p\u003e \u003cp\u003eDuffie, Singleton, Lando (DSL) Intensity Model 312\u003c\/p\u003e \u003cp\u003eCredit Rating Transition Models 329\u003c\/p\u003e \u003cp\u003eDefault Probability Density Version of Intensity Models (Hull-White) 340\u003c\/p\u003e \u003cp\u003eGeneric Credit Curves 348\u003c\/p\u003e \u003cp\u003eConclusion 353\u003c\/p\u003e \u003cp\u003eAppendix 6A: Kalman Filter 354\u003c\/p\u003e \u003cp\u003eAppendix 6B: Sample Transition Matrices 357\u003c\/p\u003e \u003cp\u003eReview Questions 358\u003c\/p\u003e \u003cp\u003eExercises 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 PD Model Validation 361\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Basics: Parameter Robustness 367\u003c\/p\u003e \u003cp\u003eMeasures of Model Power 371\u003c\/p\u003e \u003cp\u003eMeasures of PD Levels and Calibration 379\u003c\/p\u003e \u003cp\u003eSample Size and Confidence Bounds 396\u003c\/p\u003e \u003cp\u003eAssessing the Economic Value of More Powerful PD Models 418\u003c\/p\u003e \u003cp\u003eAvoiding Overfitting: A Walk-Forward Approach to Model Testing 431\u003c\/p\u003e \u003cp\u003eConclusion 437\u003c\/p\u003e \u003cp\u003eAppendix 7A: Type I and Type II Error: Converting CAP Plots into Contingency Tables 438\u003c\/p\u003e \u003cp\u003eAppendix 7B: The Likelihood for the General Case of a Default Model 440\u003c\/p\u003e \u003cp\u003eAppendix 7C: Tables of ROC ε and n max 441\u003c\/p\u003e \u003cp\u003eAppendix 7D: Proof of the Relationship between NPV Terms and ROC Terms 441\u003c\/p\u003e \u003cp\u003eAppendix 7E: Derivation of Minimum Sample Size Required to Test for Default Rate Accuracy in Uncorrelated Case 446\u003c\/p\u003e \u003cp\u003eAppendix 7F: Tables for Lower Bounds of ε and N on Probabilities of Default 447\u003c\/p\u003e \u003cp\u003eReview Questions 452\u003c\/p\u003e \u003cp\u003eExercises 452\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Portfolio Models 455\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Structural Model of Default Risk 460\u003c\/p\u003e \u003cp\u003eMeasurement of Portfolio Diversification 460\u003c\/p\u003e \u003cp\u003ePortfolio Risk Assuming No Credit Migration 461\u003c\/p\u003e \u003cp\u003eStructural Models of Default Correlation 465\u003c\/p\u003e \u003cp\u003eCredit Migration 470\u003c\/p\u003e \u003cp\u003eA Model of Value Correlation 475\u003c\/p\u003e \u003cp\u003eProbability of Large Losses 481\u003c\/p\u003e \u003cp\u003eValuation 484\u003c\/p\u003e \u003cp\u003eReturn Calculations 488\u003c\/p\u003e \u003cp\u003eRisk Calculations 491\u003c\/p\u003e \u003cp\u003ePortfolio Loss Distribution 498\u003c\/p\u003e \u003cp\u003eCapital 514\u003c\/p\u003e \u003cp\u003eEconomic Capital and Portfolio Management 519\u003c\/p\u003e \u003cp\u003eImproving Portfolio Performance 521\u003c\/p\u003e \u003cp\u003ePerformance Metrics 526\u003c\/p\u003e \u003cp\u003eReduced-Form Models and Portfolio Modeling 530\u003c\/p\u003e \u003cp\u003eCorrelation in Intensity Models 531\u003c\/p\u003e \u003cp\u003eCopulas 534\u003c\/p\u003e \u003cp\u003eFrailty 536\u003c\/p\u003e \u003cp\u003eIntegrating Market and Credit Risk 541\u003c\/p\u003e \u003cp\u003eCounterparty Risk in Credit Default Swaps (CDS) and Credit Portfolios 544\u003c\/p\u003e \u003cp\u003eConclusion 546\u003c\/p\u003e \u003cp\u003eReview Questions 547\u003c\/p\u003e \u003cp\u003eExercises 548\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Building a Better Bank: A Case Study 551\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDescription 552\u003c\/p\u003e \u003cp\u003eCurrent Organization 554\u003c\/p\u003e \u003cp\u003eTransforming the Capital Allocation Process 556\u003c\/p\u003e \u003cp\u003ePortfolio Analysis 558\u003c\/p\u003e \u003cp\u003eActive Credit Portfolio Management (ACPM) 562\u003c\/p\u003e \u003cp\u003eData, Systems, and Metrics 563\u003c\/p\u003e \u003cp\u003eACPM and Transforming the Bank 566\u003c\/p\u003e \u003cp\u003eAppendix: Figures 569\u003c\/p\u003e \u003cp\u003eExercises 574\u003c\/p\u003e \u003cp\u003eReferences 575\u003c\/p\u003e \u003cp\u003eAbout the Authors 589\u003c\/p\u003e \u003cp\u003eIndex 591\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eJEFFREY R. BOHN, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e leads the Financial Strategies group at Shinsei Bank in Tokyo. Previously, he led Moody's KMV's (MKMV's) Global Research group and MKMV's Credit Strategies group. After Moody's acquired KMV, he and Roger Stein coheaded MKMV's research and product development. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eROGER M. STEIN, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Group Managing Director of the newly formed Quantitative Research and Analytics group at Moody's Investors Service in New York. Previously, he was head of research for Moody's Risk Management Services. After Moody's acquired KMV, he and Jeffrey Bohn co-headed MKMV's research and product development.    \u003c\/p\u003e\u003cp\u003eDespite numerous advances in the world of credit—ranging from new methods for analyzing, managing, and trading credit risk to innovations in the structure of and markets for bank loans, bonds, and credit derivatives—there is still much room for improvement. \u003c\/p\u003e\u003cp\u003eWith almost twenty years of experience in the credit arena, authors Jeffrey Bohn and Roger Stein are well versed in both the theory and practice of active credit portfolio management (ACPM). The models and systems their teams have developed are in use in hundreds of large and small financial institutions worldwide. In this detailed field guide, they lay out the steps for actually implementing approaches to ACPM in today's dynamic business environment and discuss how financial institutions of all sizes can benefit from more prudent use of quantitative credit and portfolio management. \u003c\/p\u003e\u003cp\u003eFilled with in-depth insights and expert advice, \u003cem\u003eActive Credit Portfolio Management in Practice\u003c\/em\u003e opens with an informative introduction to credit analysis, credit portfolio management, and a number of organizational issues associated with ACPM in practice. The authors then move on to discuss a variety of probability of default (PD) and valuation models used in credit portfolio management systems—including structural, econometric, and reduced-form—as well as exploring some practical approaches to modeling loss given default (LGD). And since differentiating the usefulness of models is key to effective system implementation, Bohn and Stein have dedicated an entire chapter to model validation. \u003c\/p\u003e\u003cp\u003eThey then demonstrate how all of these pieces come together as they address practical strategies for credit portfolio modeling by focusing on estimating correlation and credit loss distributions. The final chapter puts all of these topics in perspective, by presenting a case study of a bank implementing the tools to build an ACPM and economic capital allocation function. This case study is drawn from a number of the actual implementations Bohn and Stein have participated in, and highlights the range of issues that often go beyond just choosing models when rolling out these systems in practice. \u003c\/p\u003e\u003cp\u003eThe book also contains supplemental material to complement it and facilitate its use for either classroom instruction or self-study. In particular, each chapter ends with review questions and exercises. Additional information—including source code—is located on the authors' companion Web site: www.creditrisklib.com. \u003c\/p\u003e\u003cp\u003eFinancial institutions without the infrastructure to measure, monitor, and manage their credit exposure run the risk of sudden and large credit losses. \u003cem\u003eActive Credit Portfolio Management in Practice\u003c\/em\u003e presents a framework for understanding and selectively implementing effective credit risk management and credit portfolio management systems—one which can help organizations better position themselves in this evolving environment.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988657586405,"sku":"NP9780470080184","price":100.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470080184.jpg?v=1761781145","url":"https:\/\/k12savings.com\/products\/active-credit-portfolio-management-in-practice-isbn-9780470080184","provider":"K12savings","version":"1.0","type":"link"}