| Description |
1 online resource |
| Contents |
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 |
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Two-Dimensional AnalysisHighlights 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 |
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Advantages of Statistical Credit ScoringTechniques 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 |
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Chapter 7: Probabilities of Default: Continuous-Time Hazard ModelsIntroduction; 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 |
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Chapter 9: Default Correlations and Credit Portfolio RiskIntroduction; 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 |
| Summary |
The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management. Combining theory with practice, this book walks you through the fundamentals of credit risk management and shows you how to implement these concepts using the SAS credit risk management program, with helpful code provided. Coverage includes data analysis and preprocessing, credit scoring; PD and LGD estimation and forecasting, low default portfolios, correlation modeling and estimation, validation, implementation of prudential regulation, stress testing of existing modeling concepts, and more, to provide a one-stop tutorial and reference for credit risk analytics. The companion website offers examples of both real and simulated credit portfolio data to help you more easily implement the concepts discussed, and the expert author team provides practical insight on this real-world intersection of finance, statistics, and analytics. SAS is the preferred software for credit risk modeling due to its functionality and ability to process large amounts of data. This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate credit risk management models.-Understand the general concepts of credit risk management -Validate and stress-test existing models -Access working examples based on both real and simulated data -Learn useful code for implementing and validating models in SAS Despite the high demand for in-house models, there is little comprehensive training available; practitioners are left to comb through piece-meal resources, executive training courses, and consultancies to cobble together the information they need. This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process |
| Notes |
Includes index |
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Print version record and CIP data provided by publisher |
| SUBJECT |
SAS (Computer file) http://id.loc.gov/authorities/names/n88028236
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SAS (Computer file) fast |
| Subject |
Credit -- Management -- Data processing
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Risk management -- Data processing
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Bank loans -- Data processing.
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BUSINESS & ECONOMICS -- Finance.
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Bank loans -- Data processing
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Credit -- Management -- Data processing
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Risk management -- Data processing
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| Form |
Electronic book
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| Author |
Roesch, Daniel, 1968- author.
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Scheule, Harald, author
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| LC no. |
2016035372 |
| ISBN |
9781119278344 |
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1119278341 |
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9781119278283 |
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1119278287 |
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9781119449560 |
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1119449561 |
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1119143985 |
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9781119143987 |
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