000			07629cam a2200733 i 4500
005			20250919140358.0
006			m o d
007			cr |||||||||||
008			190918s2016 nju o 001 0 eng
010			_a2016-035372
020			_a9781119278344 (pdf)
020			_a1119278341 (pdf)
020			_a9781119278283 (epub)
020			_a1119278287 (epub)
020			_z9781119143987 (cloth)
020			_a9781119449560
020			_a1119449561
035			_a(OCoLC)954720033 _z(OCoLC)959329206 _z(OCoLC)959596340
035			_a(OCoLC)ocn954720033
039		9	_a201911041641 _bros _y09-18-2019 _zhafiz _wUKM UBCM Wiley MARC (363 titles).mrc _x312
040			_aDLC _beng _erda _cDLC _dOCLCF _dYDX _dNST _dIDEBK _dEBLCP _dRECBK _dZ@L _dIDB _dDG1 _dOCLCO _dOCLCQ _dMERUC
042			_apcc
049			_aMAIN
050	0	0	_aHG3751
072		7	_aBUS _x027000 _2bisacsh
082	0	0	_a332.10285/555 _223
100	1		_aBaesens, Bart, _eauthor.
245	1	0	_aCredit risk analytics : _bmeasurement techniques, applications, and examples in SAS / _cBart Baesens, Daniel Roesch, Harald Scheule.
264		1	_aHoboken, New Jersey : _bWiley, _c[2016]
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bn _2rdamedia
338			_aonline resource _bnc _2rdacarrier
490	0		_aWiley & SAS business series
500			_aIncludes index.
505	0		_aTitle 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.
505	8		_aTwo-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.
505	8		_aAdvantages 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.
505	8		_aChapter 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.
505	8		_aChapter 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.
520			_aThe 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.
588			_aDescription based on print version record and CIP data provided by publisher.
630	0	0	_aSAS (Computer file)
630	0	7	_aSAS (Computer file) _2fast _0(OCoLC)fst01364029
650		0	_aCredit _xManagement _xData processing.
650		0	_aRisk management _xData processing.
650		0	_aBank loans _xData processing.
650		7	_aBUSINESS & ECONOMICS _xFinance. _2bisacsh
650		7	_aBank loans _xData processing. _2fast _0(OCoLC)fst00826707
650		7	_aCredit _xManagement _xData processing. _2fast _0(OCoLC)fst00882537
650		7	_aRisk management _xData processing. _2fast _0(OCoLC)fst01098170
655		4	_aElectronic books.
700	1		_aRoesch, Daniel, _d1968- _eauthor.
700	1		_aScheule, Harald, _eauthor.
773	0		_tWiley e-books
776	0	8	_iPrint version: _aBaesens, Bart, author. _tCredit risk analytics _dHoboken, New Jersey : John Wiley & Sons, Inc., [2016] _z9781119143987 _w(DLC) 2016024803
856	4	0	_uhttps://eresourcesptsl.ukm.remotexs.co/user/login?url=https://doi.org/10.1002/9781119449560 _zWiley Online Library
907			_a.b16758109 _b2022-11-04 _c2019-11-12
942			_n0
914			_avtls003651467
998			_ae _b2019-05-09 _cm _dz _feng _gnju _y0 _z.b16758109
999			_c643697 _d643697