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

By: Material type: TextTextPublication details: 2017 Wiley New DelhiDescription: xiv, 498 p Pbk; illustrations ; 24 cmISBN:
  • 978-8126567027
Subject(s): DDC classification:
  • 332.102855 BAE
Contents:
Chapter 1 Introduction to Credit Risk Analytics Chapter 2 Introduction to SAS Software Chapter 3 Exploratory Data Analysis Chapter 4 Data Preprocessing for Credit Risk Modeling Chapter 5 Credit Scoring Chapter 6 Probabilities of Default (PD): Discrete-Time Hazard Models Chapter 7 Probabilities of Default: Continuous-Time Hazard Models Chapter 8 Low Default Portfolios Chapter 9 Default Correlations and Credit Portfolio Risk Chapter 10 Loss Given Default (LGD) and Recovery Rates Chapter 11 Exposure at Default (EAD) and Adverse Selection Chapter 12 Bayesian Methods for Credit Risk Modeling Chapter 13 Model Validation Chapter 14 Stress Testing Chapter 15 Concluding Remarks .
Summary: 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.-
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Chapter 1 Introduction to Credit Risk Analytics
Chapter 2 Introduction to SAS Software
Chapter 3 Exploratory Data Analysis
Chapter 4 Data Preprocessing for Credit Risk Modeling
Chapter 5 Credit Scoring
Chapter 6 Probabilities of Default (PD): Discrete-Time Hazard Models
Chapter 7 Probabilities of Default: Continuous-Time Hazard Models
Chapter 8 Low Default Portfolios
Chapter 9 Default Correlations and Credit Portfolio Risk
Chapter 10 Loss Given Default (LGD) and Recovery Rates
Chapter 11 Exposure at Default (EAD) and Adverse Selection
Chapter 12 Bayesian Methods for Credit Risk Modeling
Chapter 13 Model Validation
Chapter 14 Stress Testing
Chapter 15 Concluding Remarks .

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.-

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