Credit Risk Analytics

Credit Risk Analytics
Author: Bart Baesens
Publisher: John Wiley & Sons
Total Pages: 517
Release: 2016-10-03
Genre: Business & Economics
ISBN: 1119143985
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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.

Credit Risk Analytics

Credit Risk Analytics
Author: Bart Baesens
Publisher: John Wiley & Sons
Total Pages: 516
Release: 2016-09-19
Genre: Business & Economics
ISBN: 1119278341
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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.

Advanced Credit Risk Analysis and Management

Advanced Credit Risk Analysis and Management
Author: Ciby Joseph
Publisher: John Wiley & Sons
Total Pages: 454
Release: 2013-04-22
Genre: Business & Economics
ISBN: 111860489X
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Credit is essential in the modern world and creates wealth, provided it is used wisely. The Global Credit Crisis during 2008/2009 has shown that sound understanding of underlying credit risk is crucial. If credit freezes, almost every activity in the economy is affected. The best way to utilize credit and get results is to understand credit risk. Advanced Credit Risk Analysis and Management helps the reader to understand the various nuances of credit risk. It discusses various techniques to measure, analyze and manage credit risk for both lenders and borrowers. The book begins by defining what credit is and its advantages and disadvantages, the causes of credit risk, a brief historical overview of credit risk analysis and the strategic importance of credit risk in institutions that rely on claims or debtors. The book then details various techniques to study the entity level credit risks, including portfolio level credit risks. Authored by a credit expert with two decades of experience in corporate finance and corporate credit risk, the book discusses the macroeconomic, industry and financial analysis for the study of credit risk. It covers credit risk grading and explains concepts including PD, EAD and LGD. It also highlights the distinction with equity risks and touches on credit risk pricing and the importance of credit risk in Basel Accords I, II and III. The two most common credit risks, project finance credit risk and working capital credit risk, are covered in detail with illustrations. The role of diversification and credit derivatives in credit portfolio management is considered. It also reflects on how the credit crisis develops in an economy by referring to the bubble formation. The book links with the 2008/2009 credit crisis and carries out an interesting discussion on how the credit crisis may have been avoided by following the fundamentals or principles of credit risk analysis and management. The book is essential for both lenders and borrowers. Containing case studies adapted from real life examples and exercises, this important text is practical, topical and challenging. It is useful for a wide spectrum of academics and practitioners in credit risk and anyone interested in commercial and corporate credit and related products.

Credit Risk Analytics

Credit Risk Analytics
Author: Harald Scheule
Publisher: Createspace Independent Publishing Platform
Total Pages: 264
Release: 2017-11-23
Genre: Bank loans
ISBN: 9781977760869
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Credit risk analytics in R will enable you to build credit risk models from start to finish. Accessing real credit data via the accompanying website www.creditriskanalytics.net, you will master a wide range of applications, including building your own PD, LGD and EAD models as well as mastering industry challenges such as reject inference, low default portfolio risk modeling, model validation and stress testing. This book has been written as a companion to Baesens, B., Roesch, D. and Scheule, H., 2016. Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS. John Wiley & Sons.

Credit Risk Management

Credit Risk Management
Author: Tony Van Gestel
Publisher: Oxford University Press
Total Pages: 552
Release: 2009
Genre: Business & Economics
ISBN: 0199545111
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This first of three volumes on credit risk management, providing a thorough introduction to financial risk management and modelling.

Data Science and Risk Analytics in Finance and Insurance

Data Science and Risk Analytics in Finance and Insurance
Author: Tze Leung Lai
Publisher: CRC Press
Total Pages: 1098
Release: 2024-10-02
Genre: Business & Economics
ISBN: 1351643258
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This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics. Key Features: Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks. Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections. Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors. Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics. Includes supplements and exercises to facilitate deeper comprehension.

The Handbook of Credit Risk Management

The Handbook of Credit Risk Management
Author: Sylvain Bouteille
Publisher: John Wiley & Sons
Total Pages: 44
Release: 2012-12-17
Genre: Business & Economics
ISBN: 1118300203
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A comprehensive guide to credit risk management The Handbook of Credit Risk Management presents a comprehensive overview of the practice of credit risk management for a large institution. It is a guide for professionals and students wanting a deeper understanding of how to manage credit exposures. The Handbook provides a detailed roadmap for managing beyond the financial analysis of individual transactions and counterparties. Written in a straightforward and accessible style, the authors outline how to manage a portfolio of credit exposures--from origination and assessment of credit fundamentals to hedging and pricing. The Handbook is relevant for corporations, pension funds, endowments, asset managers, banks and insurance companies alike. Covers the four essential aspects of credit risk management: Origination, Credit Risk Assessment, Portfolio Management and Risk Transfer. Provides ample references to and examples of credit market services as a resource for those readers having credit risk responsibilities. Designed for busy professionals as well as finance, risk management and MBA students. As financial transactions grow more complex, proactive management of credit portfolios is no longer optional for an institution, but a matter of survival.

The Practice of Lending

The Practice of Lending
Author: Terence M. Yhip
Publisher: Springer Nature
Total Pages: 468
Release: 2020-02-25
Genre: Business & Economics
ISBN: 3030321975
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This book provides a comprehensive treatment of credit risk assessment and credit risk rating that meets the Advanced Internal Risk-Based (AIRB) approach of Basel II. Credit risk analysis looks at many risks and this book covers all the critical areas that credit professionals need to know, including country analysis, industry analysis, financial analysis, business analysis, and management analysis. Organized under two methodological approaches to credit analysis—a criteria-based approach, which is a hybrid of expert judgement and purely mathematical methodologies, and a mathematical approach using regression analysis to model default probability—the book covers a cross-section of industries including passenger airline, commercial real estate, and commercial banking. In three parts, the sections focus on hybrid models, statistical models, and credit management. While the book provides theory and principles, its emphasis is on practical applications, and will appeal to credit practitioners in the banking and investment community alongside college and university students who are preparing for a career in lending.

Credit-Risk Modelling

Credit-Risk Modelling
Author: David Jamieson Bolder
Publisher: Springer
Total Pages: 684
Release: 2018-10-31
Genre: Business & Economics
ISBN: 3319946889
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The risk of counterparty default in banking, insurance, institutional, and pension-fund portfolios is an area of ongoing and increasing importance for finance practitioners. It is, unfortunately, a topic with a high degree of technical complexity. Addressing this challenge, this book provides a comprehensive and attainable mathematical and statistical discussion of a broad range of existing default-risk models. Model description and derivation, however, is only part of the story. Through use of exhaustive practical examples and extensive code illustrations in the Python programming language, this work also explicitly shows the reader how these models are implemented. Bringing these complex approaches to life by combining the technical details with actual real-life Python code reduces the burden of model complexity and enhances accessibility to this decidedly specialized field of study. The entire work is also liberally supplemented with model-diagnostic, calibration, and parameter-estimation techniques to assist the quantitative analyst in day-to-day implementation as well as in mitigating model risk. Written by an active and experienced practitioner, it is an invaluable learning resource and reference text for financial-risk practitioners and an excellent source for advanced undergraduate and graduate students seeking to acquire knowledge of the key elements of this discipline.

Python for Finance

Python for Finance
Author: Yuxing Yan
Publisher: Packt Publishing Ltd
Total Pages: 586
Release: 2017-06-30
Genre: Computers
ISBN: 1787125025
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Learn and implement various Quantitative Finance concepts using the popular Python libraries About This Book Understand the fundamentals of Python data structures and work with time-series data Implement key concepts in quantitative finance using popular Python libraries such as NumPy, SciPy, and matplotlib A step-by-step tutorial packed with many Python programs that will help you learn how to apply Python to finance Who This Book Is For This book assumes that the readers have some basic knowledge related to Python. However, he/she has no knowledge of quantitative finance. In addition, he/she has no knowledge about financial data. What You Will Learn Become acquainted with Python in the first two chapters Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models Learn how to price a call, put, and several exotic options Understand Monte Carlo simulation, how to write a Python program to replicate the Black-Scholes-Merton options model, and how to price a few exotic options Understand the concept of volatility and how to test the hypothesis that volatility changes over the years Understand the ARCH and GARCH processes and how to write related Python programs In Detail This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM's market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option. Style and approach This book takes a step-by-step approach in explaining the libraries and modules in Python, and how they can be used to implement various aspects of quantitative finance. Each concept is explained in depth and supplemented with code examples for better understanding.