{"product_id":"credit-risk-modeling-using-excel-and-vba-isbn-9780470660928","title":"Credit Risk Modeling using Excel and VBA","description":"It is common to blame the inadequacy of credit risk models for the fact that the financial crisis has caught many market participants by surprise. On closer inspection, though, it often appears that market participants failed to understand or to use the models correctly. The recent events therefore do not invalidate traditional credit risk modeling as described in the first edition of the book. A second edition is timely, however, because the first dealt relatively briefly with instruments featuring prominently in the crisis (CDSs and CDOs). In addition to expanding the coverage of these instruments, the book will focus on modeling aspects which were of particular relevance in the financial crisis (e.g. estimation error) and demonstrate the usefulness of credit risk modelling through case studies. \u003cp\u003eThis book provides practitioners and students with an intuitive, hands-on introduction to modern credit risk modelling. Every chapter starts with an explanation of the methodology and then the authors take the reader step by step through the implementation of the methods in Excel and VBA.  They focus specifically on risk management issues and cover default probability estimation (scoring, structural models, and transition matrices), correlation and portfolio analysis, validation, as well as credit default swaps and structured finance.\u003c\/p\u003e \u003cp\u003eThe book has an accompanying website, \u003ca href=\"https:\/\/urldefense.com\/v3\/__https:\/creditriskmodeling.wordpress.com\/__;!!N11eV2iwtfs!su_4F0Lsf2YugC4rdKyKyqN_q3UYiLfqwJE402PxwaKK5Ot5ok05oegepiCeSyJ5IC5KPUJuvmEsSkZ2FvG-RMRnkF5K$\"\u003ehttps:\/\/creditriskmodeling.wordpress.com\/\u003c\/a\u003e, which has been specially updated for this Second Edition and contains slides and exercises for lecturers.\u003c\/p\u003e \u003cp\u003ePreface to the 2nd edition xi\u003c\/p\u003e \u003cp\u003ePreface to the 1st edition xiii\u003c\/p\u003e \u003cp\u003eSome Hints for Troubleshooting xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Estimating Credit Scores with Logit 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLinking scores, default probabilities and observed default behavior 1\u003c\/p\u003e \u003cp\u003eEstimating logit coefficients in Excel 4\u003c\/p\u003e \u003cp\u003eComputing statistics after model estimation 8\u003c\/p\u003e \u003cp\u003eInterpreting regression statistics 10\u003c\/p\u003e \u003cp\u003ePrediction and scenario analysis 12\u003c\/p\u003e \u003cp\u003eTreating outliers in input variables 16\u003c\/p\u003e \u003cp\u003eChoosing the functional relationship between the score and explanatory variables 20\u003c\/p\u003e \u003cp\u003eConcluding remarks 25\u003c\/p\u003e \u003cp\u003eAppendix 25\u003c\/p\u003e \u003cp\u003eLogit and probit 25\u003c\/p\u003e \u003cp\u003eMarginal effects 25\u003c\/p\u003e \u003cp\u003eNotes and literature 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Structural Approach to Default Prediction and Valuation 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefault and valuation in a structural model 27\u003c\/p\u003e \u003cp\u003eImplementing the Merton model with a one-year horizon 30\u003c\/p\u003e \u003cp\u003eThe iterative approach 30\u003c\/p\u003e \u003cp\u003eA solution using equity values and equity volatilities 35\u003c\/p\u003e \u003cp\u003eImplementing the Merton model with a T -year horizon 39\u003c\/p\u003e \u003cp\u003eCredit spreads 43\u003c\/p\u003e \u003cp\u003eCreditGrades 44\u003c\/p\u003e \u003cp\u003eAppendix 50\u003c\/p\u003e \u003cp\u003eNotes and literature 52\u003c\/p\u003e \u003cp\u003eAssumptions 52\u003c\/p\u003e \u003cp\u003eLiterature 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Transition Matrices 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCohort approach 56\u003c\/p\u003e \u003cp\u003eMulti-period transitions 61\u003c\/p\u003e \u003cp\u003eHazard rate approach 63\u003c\/p\u003e \u003cp\u003eObtaining a generator matrix from a given transition matrix 69\u003c\/p\u003e \u003cp\u003eConfidence intervals with the binomial distribution 71\u003c\/p\u003e \u003cp\u003eBootstrapped confidence intervals for the hazard approach 74\u003c\/p\u003e \u003cp\u003eNotes and literature 78\u003c\/p\u003e \u003cp\u003eAppendix 78\u003c\/p\u003e \u003cp\u003eMatrix functions 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Prediction of Default and Transition Rates 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCandidate variables for prediction 83\u003c\/p\u003e \u003cp\u003ePredicting investment-grade default rates with linear regression 85\u003c\/p\u003e \u003cp\u003ePredicting investment-grade default rates with Poisson regression 88\u003c\/p\u003e \u003cp\u003eBacktesting the prediction models 94\u003c\/p\u003e \u003cp\u003ePredicting transition matrices 99\u003c\/p\u003e \u003cp\u003eAdjusting transition matrices 100\u003c\/p\u003e \u003cp\u003eRepresenting transition matrices with a single parameter 101\u003c\/p\u003e \u003cp\u003eShifting the transition matrix 103\u003c\/p\u003e \u003cp\u003eBacktesting the transition forecasts 108\u003c\/p\u003e \u003cp\u003eScope of application 108\u003c\/p\u003e \u003cp\u003eNotes and literature 110\u003c\/p\u003e \u003cp\u003eAppendix 110\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Prediction of Loss Given Default 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCandidate variables for prediction 115\u003c\/p\u003e \u003cp\u003eInstrument-related variables 116\u003c\/p\u003e \u003cp\u003eFirm-specific variables 117\u003c\/p\u003e \u003cp\u003eMacroeconomic variables 118\u003c\/p\u003e \u003cp\u003eIndustry variables 118\u003c\/p\u003e \u003cp\u003eCreating a data set 119\u003c\/p\u003e \u003cp\u003eRegression analysis of LGD 120\u003c\/p\u003e \u003cp\u003eBacktesting predictions 123\u003c\/p\u003e \u003cp\u003eNotes and literature 126\u003c\/p\u003e \u003cp\u003eAppendix 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Modeling and Estimating Default Correlations with the Asset Value Approach 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefault correlation, joint default probabilities and the asset value approach 131\u003c\/p\u003e \u003cp\u003eCalibrating the asset value approach to default experience: the method of moments 133\u003c\/p\u003e \u003cp\u003eEstimating asset correlation with maximum likelihood 136\u003c\/p\u003e \u003cp\u003eExploring the reliability of estimators with a Monte Carlo study 144\u003c\/p\u003e \u003cp\u003eConcluding remarks 147\u003c\/p\u003e \u003cp\u003eNotes and literature 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Measuring Credit Portfolio Risk with the Asset Value Approach 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA default-mode model implemented in the spreadsheet 149\u003c\/p\u003e \u003cp\u003eVBA implementation of a default-mode model 152\u003c\/p\u003e \u003cp\u003eImportance sampling 156\u003c\/p\u003e \u003cp\u003eQuasi Monte Carlo 160\u003c\/p\u003e \u003cp\u003eAssessing Simulation Error 162\u003c\/p\u003e \u003cp\u003eExploiting portfolio structure in the VBA program 165\u003c\/p\u003e \u003cp\u003eDealing with parameter uncertainty 168\u003c\/p\u003e \u003cp\u003eExtensions 170\u003c\/p\u003e \u003cp\u003eFirst extension: Multi-factor model 170\u003c\/p\u003e \u003cp\u003eSecond extension: t-distributed asset values 171\u003c\/p\u003e \u003cp\u003eThird extension: Random LGDs 173\u003c\/p\u003e \u003cp\u003eFourth extension: Other risk measures 175\u003c\/p\u003e \u003cp\u003eFifth extension: Multi-state modeling 177\u003c\/p\u003e \u003cp\u003eNotes and literature 179\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Validation of Rating Systems 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCumulative accuracy profile and accuracy ratios 182\u003c\/p\u003e \u003cp\u003eReceiver operating characteristic (ROC) 185\u003c\/p\u003e \u003cp\u003eBootstrapping confidence intervals for the accuracy ratio 187\u003c\/p\u003e \u003cp\u003eInterpreting caps and ROCs 190\u003c\/p\u003e \u003cp\u003eBrier score 191\u003c\/p\u003e \u003cp\u003eTesting the calibration of rating-specific default probabilities 192\u003c\/p\u003e \u003cp\u003eValidation strategies 195\u003c\/p\u003e \u003cp\u003eTesting for missing information 198\u003c\/p\u003e \u003cp\u003eNotes and literature 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Validation of Credit Portfolio Models 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTesting distributions with the Berkowitz test 203\u003c\/p\u003e \u003cp\u003eExample implementation of the Berkowitz test 206\u003c\/p\u003e \u003cp\u003eRepresenting the loss distribution 207\u003c\/p\u003e \u003cp\u003eSimulating the critical chi-square value 209\u003c\/p\u003e \u003cp\u003eTesting modeling details: Berkowitz on subportfolios 211\u003c\/p\u003e \u003cp\u003eAssessing power 214\u003c\/p\u003e \u003cp\u003eScope and limits of the test 216\u003c\/p\u003e \u003cp\u003eNotes and literature 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Credit Default Swaps and Risk-Neutral Default Probabilities 219\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDescribing the term structure of default: PDs cumulative, marginal and seen from today 220\u003c\/p\u003e \u003cp\u003eFrom bond prices to risk-neutral default probabilities 221\u003c\/p\u003e \u003cp\u003eConcepts and formulae 221\u003c\/p\u003e \u003cp\u003eImplementation 225\u003c\/p\u003e \u003cp\u003ePricing a CDS 232\u003c\/p\u003e \u003cp\u003eRefining the PD estimation 234\u003c\/p\u003e \u003cp\u003eMarket values for a CDS 237\u003c\/p\u003e \u003cp\u003eExample 239\u003c\/p\u003e \u003cp\u003eEstimating upfront CDS and the ‘Big Bang’ protocol 240\u003c\/p\u003e \u003cp\u003ePricing of a pro-rata basket 241\u003c\/p\u003e \u003cp\u003eForward CDS spreads 242\u003c\/p\u003e \u003cp\u003eExample 243\u003c\/p\u003e \u003cp\u003ePricing of swaptions 243\u003c\/p\u003e \u003cp\u003eNotes and literature 247\u003c\/p\u003e \u003cp\u003eAppendix 247\u003c\/p\u003e \u003cp\u003eDeriving the hazard rate for a CDS 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Risk Analysis and Pricing of Structured Credit: CDOs and First-to-Default\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSwaps 249\u003c\/p\u003e \u003cp\u003eEstimating CDO risk with Monte Carlo simulation 249\u003c\/p\u003e \u003cp\u003eThe large homogeneous portfolio (LHP) approximation 253\u003c\/p\u003e \u003cp\u003eSystemic risk of CDO tranches 256\u003c\/p\u003e \u003cp\u003eDefault times for first-to-default swaps 259\u003c\/p\u003e \u003cp\u003eCDO pricing in the LHP framework 263\u003c\/p\u003e \u003cp\u003eSimulation-based CDO pricing 272\u003c\/p\u003e \u003cp\u003eNotes and literature 281\u003c\/p\u003e \u003cp\u003eAppendix 282\u003c\/p\u003e \u003cp\u003eClosed-form solution for the LHP model 282\u003c\/p\u003e \u003cp\u003eCholesky decomposition 283\u003c\/p\u003e \u003cp\u003eEstimating PD structure from a CDS 284\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Basel II and Internal Ratings 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCalculating capital requirements in the Internal Ratings-Based (IRB) approach 285\u003c\/p\u003e \u003cp\u003eAssessing a given grading structure 288\u003c\/p\u003e \u003cp\u003eTowards an optimal grading structure 294\u003c\/p\u003e \u003cp\u003eNotes and literature 297\u003c\/p\u003e \u003cp\u003eAppendix A1 Visual Basics for Applications (VBA) 299\u003c\/p\u003e \u003cp\u003eAppendix A2 Solver 307\u003c\/p\u003e \u003cp\u003eAppendix A3 Maximum Likelihood Estimation and Newton’s Method 313\u003c\/p\u003e \u003cp\u003eAppendix A4 Testing and Goodness of Fit 319\u003c\/p\u003e \u003cp\u003eAppendix A5 User-defined Functions 325\u003c\/p\u003e \u003cp\u003eIndex 333\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eAbout the authors\u003c\/i\u003e  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eGUNTER LÖFFLER\u003c\/b\u003e is Professor of Finance at the University of Ulm in Germany. His current research interests are on credit risk and empirical finance. Previously, Gunter was Assistant Professor at Goethe University Frankfurt, and served as an internal consultant in the asset management division of Commerzbank. His Ph.D. in finance is from the University of Mannheim. Gunter has studied at Heidelberg and Cambridge Universities.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePETER N. POSCH\u003c\/b\u003e is Assistant Professor of Finance at the University of Ulm in Germany. Previously, Peter was co-head of credit treasury at a large bank, where he also traded credit derivatives and other fixed income products for the bank's proprietary books. His Ph.D. in finance on the dynamics of credit risk is from the University of Ulm. Peter has studied economics, philosophy and law at the University of Bonn. \u003c\/p\u003e  \u003cp\u003eCredit risk modeling using Excel and VBA \u003c\/p\u003e\u003cp\u003eSecond Edition \u003c\/p\u003e\u003cp\u003e\u003cb\u003eFurther praise for the first edition\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003e\"I read this book cover-to-cover and recommend it heartily. For each topic, there is straightforward explanation,practicalexamples, and implementable coding. This book would have saved me months of effort many times over with its full 'toolset' of Excel\/VBA code. I have immediate plans to reread sections and incorporate sections of code into my own spreadsheets.\"\u003c\/i\u003e Greg M. Gupton, Founder and Director, DefaultRisk.com   \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePraise for the second edition\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003e\"This is a very useful book. It provides incisive basic background knowledge on modelling for key credit risk topics, including a new chapter on loss given default prediction, and the coding examples help to deepen the readers' understanding and can be used as the basis for more advanced approaches, possibly with more powerful tools.\"\u003c\/i\u003e Dirk Tasche, Senior Risk Advisor, Lloyds Banking Group   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eCredit risk modeling using Excel and VBA \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eSecond Edition \u003c\/p\u003e\u003cp\u003eGunter Löffler and Peter N. Posch  \u003c\/p\u003e\u003cp\u003eThis book provides practitioners and students with a hands-on introduction to modern credit risk modeling. The authors begin each chapter with an accessible presentation of a given methodology, before providing a step-by-step guide to implementation methods in Excel and Visual Basic for Applications (VBA). The book covers default probability estimation (scoring, structural models, and transition matrices), correlation and portfolio analysis, validation, as well as credit default swaps and structured finance. Several appendices and videos increase ease of access.  \u003c\/p\u003e\u003cp\u003eThe second edition includes new coverage of the important issue of how parameter uncertainty can be dealt with in the estimation of portfolio risk, as well as comprehensive new sections on the pricing of CDSs and CDOs, and a chapter on predicting borrower-specific loss given default with regression models. In all, the authors present a host of applications – many of which go beyond standard Excel or VBA usages, for example, how to estimate logit models with maximum likelihood, or how to quickly conduct large-scale Monte Carlo simulations.  \u003c\/p\u003e\u003cp\u003eClearly written with a multitude of practical examples, the new edition of \u003ci\u003eCredit Risk Modeling using Excel and VBA\u003c\/i\u003e will prove an indispensable resource for anyone working in, studying or researching this important field.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePraise for the first edition\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003e\"In one place, Löffler and Posch provide all that is needed to install a state-of-the-art risk management system, including a broad understanding of different risk management frameworks, detailed estimation techniques for deriving PD, LGD, and correlation parameters, and programming tools for putting these methods into practice.\"\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003eRichard Cantor, Chief Credit Officer, Moody's Investors Service\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989003944165,"sku":"NP9780470660928","price":119.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470660928.jpg?v=1761782393","url":"https:\/\/k12savings.com\/es\/products\/credit-risk-modeling-using-excel-and-vba-isbn-9780470660928","provider":"K12savings","version":"1.0","type":"link"}