Advances in Credit Risk Modeling and Management

Edited by
July 2020
190 pages
  • ISBN978-3-03928-760-4 (Paperback)
  • ISBN978-3-03928-761-1 (PDF)

This book is a reprint of the Special Issue Advances in Credit Risk Modeling and Management that was published in

Business & Economics
Computer Science & Mathematics
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.
  • Paperback
© 2020 by the authors; CC BY-NC-ND license
recovery rates; beta regression; credit risk; credit risk; contingent convertible debt; financial modelling; risk management; financial crisis; recovery rate; credit risk; loss given default; model ambiguity; default time; credit risk; no-arbitrage; reduced-form HJM models; recovery process; Counterparty Credit Risk; Hidden Markov Model; Risk Factor Evolution; Backtesting; FX rate; Geometric Brownian Motion; trade credit; small and micro-enterprises; financial non-financial variables; risk assessment; logistic regression; probability of default; loss given default; wrong-way risk; dependence; urn model; counterparty risk; credit valuation adjustment (CVA); XVA (X-valuation adjustments) compression; genetic algorithm; n/a