Credit Risk Analytics : Measurement Techniques, Applications, and Examples in SAS.
Daniel. Roesch
Bok Engelsk 2016 · Electronic books.
| Omfang | 1 online resource (583 pages)
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| Utgave | 1st ed.
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| Opplysninger | Intro -- Title Page -- Copyright -- Table of Contents -- Dedication -- Acknowledgments -- About the Authors -- Chapter 1: Introduction to Credit Risk Analytics -- Why This Book Is Timely -- The Current Regulatory Regime: Basel Regulations -- Introduction to Our Data Sets -- Housekeeping -- Chapter 2: Introduction to SAS Software -- SAS versus Open Source Software -- Base SAS -- SAS/STAT -- Macros in Base SAS -- SAS Output Delivery System (ODS) -- SAS/IML -- SAS Studio -- SAS Enterprise Miner -- Other SAS Solutions for Credit Risk Management -- Reference -- Chapter 3: Exploratory Data Analysis -- Introduction -- One-Dimensional Analysis -- Two-Dimensional Analysis -- Highlights of Inductive Statistics -- Reference -- Chapter 4: Data Preprocessing for Credit Risk Modeling -- Types of Data Sources -- Merging Data Sources -- Sampling -- Types of Data Elements -- Visual Data Exploration and Exploratory Statistical Analysis -- Descriptive Statistics -- Missing Values -- Outlier Detection and Treatment -- Standardizing Data -- Categorization -- Weights of Evidence Coding -- Variable Selection -- Segmentation -- Default Definition -- Practice Questions -- Notes -- References -- Chapter 5: Credit Scoring -- Basic Concepts -- Judgmental versus Statistical Scoring -- Advantages of Statistical Credit Scoring -- Techniques to Build Scorecards -- Credit Scoring for Retail Exposures -- Reject Inference -- Credit Scoring for Nonretail Exposures -- Big Data for Credit Scoring -- Overrides -- Evaluating Scorecard Performance -- Business Applications of Credit Scoring -- Limitations -- Practice Questions -- References -- Chapter 6: Probabilities of Default (PD): Discrete-Time Hazard Models -- Introduction -- Discrete-Time Hazard Models -- Which Model Should I Choose? -- Fitting and Forecasting -- Formation of Rating Classes -- Practice Questions -- References.. - Chapter 7: Probabilities of Default: Continuous-Time Hazard Models -- Introduction -- Censoring -- Life Tables -- Cox Proportional Hazards Models -- Accelerated Failure Time Models -- Extension: Mixture Cure Modeling -- Discrete-Time Hazard versus Continuous-Time Hazard Models -- Practice Questions -- References -- Chapter 8: Low Default Portfolios -- Introduction -- Basic Concepts -- Developing Predictive Models for Skewed Data Sets -- Mapping to an External Rating Agency -- Confidence Level Based Approach -- Other Methods -- LGD and EAD for Low Default Portfolios -- Practice Questions -- References -- Chapter 9: Default Correlations and Credit Portfolio Risk -- Introduction -- Modeling Loss Distributions with Correlated Defaults -- Estimating Correlations -- Extensions -- Practice Questions -- References -- Chapter 10: Loss Given Default (LGD) and Recovery Rates -- Introduction -- Marginal LGD Models -- PD-LGD Models -- Extensions -- Practice Questions -- References -- Chapter 11: Exposure at Default (EAD) and Adverse Selection -- Introduction -- Regulatory Perspective on EAD -- EAD Modeling -- Practice Questions -- References -- Chapter 12: Bayesian Methods for Credit Risk Modeling -- Introduction -- The Bayesian Approach to Statistics -- PD Estimation with Bayesian Statistics -- Correlation Estimation with Bayesian Statistics -- PD Estimation for Low Default Portfolios -- Practice Questions -- Notes -- References -- Chapter 13: Model Validation -- Introduction -- Regulatory Perspective -- Basic Concepts of Validation -- Quantitative Validation -- Qualitative Validation -- Practice Questions -- References -- Chapter 14: Stress Testing -- Introduction -- Integration with the Basel Risk Model -- Stress Testing Applications in SAS -- Practice Questions -- References -- Chapter 15: Concluding Remarks -- Other Credit Risk Exposures.. - Limitations of Credit Risk Analytics -- Guiding Principles for Building Good Credit Risk Models -- References -- Index -- End User License Agreement.
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| ISBN | 9781119278344
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