The motivation for the mathematical modeling studied in this text on developments in credit risk research is the bridging of the gap between mathematical theory of credit risk and the financial practice. Mathematical developments are covered thoroughly and give the structural and reduced-form approaches to credit risk modeling. Included is a detailed study of various arbitrage-free models of default term structures with several rating grades.

This book offers an advanced introduction to models of credit risk valuation, concentrating on firm-value and reduced-form approaches and their application. Also included are new models for valuing derivative securities with credit risk. The book provides detailed descriptions of the state-of-the-art martingale methods and advanced numerical implementations based on multivariate trees used to price derivative credit risk. Numerical examples illustrate the effects of credit risk on the prices of financial derivatives.

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.

Contains Nearly 100 Pages of New MaterialThe recent financial crisis has shown that credit risk in particular and finance in general remain important fields for the application of mathematical concepts to real-life situations. While continuing to focus on common mathematical approaches to model credit portfolios, Introduction to Credit Risk Modelin

The definitive guide to fixed income valuation and risk analysis The Trilogy in Fixed Income Valuation and Risk Analysis comprehensively covers the most definitive work on interest rate risk, term structure analysis, and credit risk. The first book on interest rate risk modeling examines virtually every well-known IRR model used for pricing and risk analysis of various fixed income securities and their derivatives. The companion CD-ROM contain numerous formulas and programming tools that allow readers to better model risk and value fixed income securities. This comprehensive resource provides readers with the hands-on information and software needed to succeed in this financial arena.

In today's increasingly competitive financial world, successful risk management, portfolio management, and financial structuring demand more than up-to-date financial know-how. They also call for quantitative expertise, including the ability to effectively apply mathematical modeling tools and techniques. An Introduction to Credit Risk Modeling supplies both the bricks and the mortar of risk management. In a gentle and concise lecture-note style, it introduces the fundamentals of credit risk management, provides a broad treatment of the related modeling theory and methods, and explores their application to credit portfolio securitization, credit risk in a trading portfolio, and credit derivatives risk. The presentation is thorough but refreshingly accessible, foregoing unnecessary technical details yet remaining mathematically precise. Whether you are a risk manager looking for a more quantitative approach to credit risk or you are planning a move from the academic arena to a career in professional credit risk management, An Introduction to Credit Risk Modeling is the book you've been looking for. It will bring you quickly up to speed with information needed to resolve the questions and quandaries encountered in practice.