دانلود کتاب Credit risk analytics : measurement techniques, applications, and examples in SAS

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Content: 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 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. 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. 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. 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.