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Credit risk evaluation is as old as commerce itself. Processes have been refined over centuries based on cumulative experience, judgment and learning. The rapid development of financial markets however has tested the limits of the traditional approach as highly publicized credit losses and huge non-performing loans across the globe well document. Distress among many credit professionals and regulators prevails. This book describes a different and unemotional approach to credit risk evaluation. Based on abstract and objective credit models, the concept of credit risk measurement is introduced through a range of theoretical and practical perspectives. From making a case for credit risk measurement as a complement to the more traditional approaches to credit risk management, the book covers validation, applications and new areas of credit risk management. Contributions by leading academics, practitioners and consultants provide for scholars and credit risk professionals but also less mathematically inclined readers or interested parties, a wide spectrum of ideas and concepts for developing and improving their own viewpoint, processes and approaches. A demo-CD of one particular model is included for practical testing and playing with applied credit risk measurement concepts.
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Held at Oakland University, School of Business Administration, Department of Accounting and Finance. This book provides a summary of state-of-the-art methods and research in the analysis of credit. As such, it offers very useful insights into this vital area of finance, which has too often been under-researched and little-taught in academia. Including an overview of processes that are utilized for estimating the probability of default and the loss given default for a wide array of debts, the book will also be useful in evaluating individual loans and bonds, as well as managing entire portfolios of such assets. Each chapter is written by authors who presented and discussed their contemporary research and knowledge at the Third International Conference on Credit Analysis and Risk Management, held on August 21–22, 2014 at the Department of Accounting and Finance, School of Business administration, Oakland University. This collection of writings by these experts in the field is uniquely designed to enhance the understanding of credit analysis in a fashion that permits a broad perspective on the science and art of credit analysis.
The aim of this book is twofold: Firstly to focus on the development of new instruments and topics in the financial industry. Secondly to analyze the development of “old” themes applied to different international contexts, such as cross-border banking and the role of government financial resources in China. With these goals in mind, the book explores the investigation of new instruments for the financing of SMEs and new ventures, such as mini bonds and equity crowdfunding. Additionally, it covers the field of corporate governance and corporate social responsibility including financial inclusion, gender roles, disclosure, social media roles and litigation. The book also investigates the choices followed by the Royal Swedish Academy in the selection of Nobel laureates in economics science to analyze their influence on the financial industry. Geared to banking academics, researchers and students, this book uncovers the most prominent issues within the banking industry today.
The most cutting-edge read on the pricing, modeling, and management of credit risk available The rise of credit risk measurement and the credit derivatives market started in the early 1990s and has grown ever since. For many professionals, understanding credit risk measurement as a discipline is now more important than ever. Credit Risk Measurement, Second Edition has been fully revised to reflect the latest thinking on credit risk measurement and to provide credit risk professionals with a solid understanding of the alternative approaches to credit risk measurement. This readable guide discusses the latest pricing, modeling, and management techniques available for dealing with credit risk. New chapters highlight the latest generation of credit risk measurement models, including a popular class known as intensity-based models. Credit Risk Measurement, Second Edition also analyzes significant changes in banking regulations that are impacting credit risk measurement at financial institutions. With fresh insights and updated information on the world of credit risk measurement, this book is a must-read reference for all credit risk professionals. Anthony Saunders (New York, NY) is the John M. Schiff Professor of Finance and Chair of the Department of Finance at the Stern School of Business at New York University. He holds positions on the Board of Academic Consultants of the Federal Reserve Board of Governors as well as the Council of Research Advisors for the Federal National Mortgage Association. He is the editor of the Journal of Banking and Finance and the Journal of Financial Markets, Instruments and Institutions. Linda Allen (New York, NY) is Professor of Finance at Baruch College and Adjunct Professor of Finance at the Stern School of Business at New York University. She also is author of Capital Markets and Institutions: A Global View (Wiley: 0471130494). Over the years, financial professionals around the world have looked to the Wiley Finance series and its wide array of bestselling books for the knowledge, insights, and techniques that are essential to success in financial markets. As the pace of change in financial markets and instruments quickens, Wiley Finance continues to respond. With critically acclaimed books by leading thinkers on value investing, risk management, asset allocation, and many other critical subjects, the Wiley Finance series provides the financial community with information they want. Written to provide professionals and individuals with the most current thinking from the best minds in the industry, it is no wonder that the Wiley Finance series is the first and last stop for financial professionals looking to increase their financial expertise.
Credit Scoring and Its Applications is recognized as the bible of credit scoring. It contains a comprehensive review of the objectives, methods, and practical implementation of credit and behavioral scoring. The authors review principles of the statistical and operations research methods used in building scorecards, as well as the advantages and disadvantages of each approach. The book contains a description of practical problems encountered in building, using, and monitoring scorecards and examines some of the country-specific issues in bankruptcy, equal opportunities, and privacy legislation. It contains a discussion of economic theories of consumers' use of credit, and readers will gain an understanding of what lending institutions seek to achieve by using credit scoring and the changes in their objectives. New to the second edition are lessons that can be learned for operations research model building from the global financial crisis, current applications of scoring, discussions on the Basel Accords and their requirements for scoring, new methods for scorecard building and new expanded sections on ways of measuring scorecard performance. And survival analysis for credit scoring. Other unique features include methods of monitoring scorecards and deciding when to update them, as well as different applications of scoring, including direct marketing, profit scoring, tax inspection, prisoner release, and payment of fines.
Credit risk remains one of the major risks faced by most financial and credit institutions. It is deeply connected to the real economy due to the systemic nature of some banks, but also because well-managed lending facilities are key for wealth creation and technological innovation. This book is a collection of innovative papers in the field of credit risk management. Besides the probability of default (PD), the major driver of credit risk is the loss given default (LGD). In spite of its central importance, LGD modeling remains largely unexplored in the academic literature. This book proposes three contributions in the field. Ye & Bellotti exploit a large private dataset featuring non-performing loans to design a beta mixture model. Their model can be used to improve recovery rate forecasts and, therefore, to enhance capital requirement mechanisms. François uses instead the price of defaultable instruments to infer the determinants of market-implied recovery rates and finds that macroeconomic and long-term issuer specific factors are the main determinants of market-implied LGDs. Cheng & Cirillo address the problem of modeling the dependency between PD and LGD using an original, urn-based statistical model. Fadina & Schmidt propose an improvement of intensity-based default models by accounting for ambiguity around both the intensity process and the recovery rate. Another topic deserving more attention is trade credit, which consists of the supplier providing credit facilities to his customers. Whereas this is likely to stimulate exchanges in general, it also magnifies credit risk. This is a difficult problem that remains largely unexplored. Kanapickiene & Spicas propose a simple but yet practical model to assess trade credit risk associated with SMEs and microenterprises operating in Lithuania. Another topical area in credit risk is counterparty risk and all other adjustments (such as liquidity and capital adjustments), known as XVA. Chataignier & Crépey propose a genetic algorithm to compress CVA and to obtain affordable incremental figures. Anagnostou & Kandhai introduce a hidden Markov model to simulate exchange rate scenarios for counterparty risk. Eventually, Boursicot et al. analyzes CoCo bonds, and find that they reduce the total cost of debt, which is positive for shareholders. In a nutshell, all the featured papers contribute to shedding light on various aspects of credit risk management that have, so far, largely remained unexplored.
This paper provides an overview of sovereign debt portfolio risks and discusses various liability management operations (LMOs) and instruments used by public debt managers to mitigate these risks. Debt management strategies analyzed in the context of helping reach debt portfolio targets and attain desired portfolio structures. Also, the paper outlines how LMOs could be integrated into a debt management strategy and serve as policy tools to reduce potential debt portfolio vulnerabilities. Further, the paper presents operational issues faced by debt managers, including the need to develop a risk management framework, interactions of debt management with fiscal policy, monetary policy, and financial stability, as well as efficient government bond markets.
Advanced Credit Analysis presents the latest and most advanced modelling techniques in the theory and practice of credit risk pricing and management. The book stresses the logic of theoretical models from the structural and the reduced-form kind, their applications and extensions. It shows the mathematical models that help determine optimal collateralisation and marking-to-market policies. It looks at modern credit risk management tools and the current structuring techniques available with credit derivatives.