The estimation and the validation of the Basel II risk parameters PD (default probability), LGD (loss given fault), and EAD (exposure at default) is an important problem in banking practice. These parameters are used on the one hand as inputs to credit portfolio models and in loan pricing frameworks, on the other to compute regulatory capital according to the new Basel rules. This book covers the state-of-the-art in designing and validating rating systems and default probability estimations. Furthermore, it presents techniques to estimate LGD and EAD and includes a chapter on stress testing of the Basel II risk parameters. The second edition is extended by three chapters explaining how the Basel II risk parameters can be used for building a framework for risk-adjusted pricing and risk management of loans.
A critical problem in the practice of banking risk assessment is the estimation and validation of the Basel II risk parameters PD (default probability), LGD (loss given default), and EAD (exposure at default). This book presents the state-of-the-art in designing and validating rating systems and default probability estimations, and outlines techniques to estimate LGD and EAD. Also included is a chapter on stress testing of the Basel II risk parameters.
In this paper, we provide an overview of the concerns surrounding the variations in the calculation of risk-weighted assets (RWAs) across banks and jurisdictions and how this might undermine the Basel III capital adequacy framework. We discuss the key drivers behind the differences in these calculations, drawing upon a sample of systemically important banks from Europe, North America, and Asia Pacific. We then discuss a range of policy options that could be explored to fix the actual and perceived problems with RWAs, and improve the use of risk-sensitive capital ratios.
This book is a one-stop-shop reference for risk management practitioners involved in the validation of risk models. It is a comprehensive manual about the tools, techniques and processes to be followed, focused on all the models that are relevant in the capital requirements and supervisory review of large international banks.
Guide to Optimal Operational Risk and Basel II presents the key aspects of operational risk management that are also aligned with the Basel II requirements. This volume provides detailed guidance for the design and implementation of an efficient operational risk management system. It contains all elements of assessment, including operational risk i
Comprehensively researched, this volume assists and advises the financial practitioner of every possible consequence of the latest Basel Accord - including advice on the implementation of systems affected by the Accord's various regulations.
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.
The dependency structure of credit risk parameters is a key driver for capital consumption and receives regulatory and scientific attention. The impact of parameter imperfections on the quality of expected loss (EL) in the sense of a fair, unbiased estimate of risk expenses, however, is barely covered. So far there are no established backtesting procedures for EL that quantify its impact with regards to pricing or risk-adjusted profitability measures. In this paper, a practically oriented, top-down approach to assessing the quality of EL by backtesting with a properly defined risk measure is introduced. In a first step, the concept of risk expenses ("Cost of Risk") has to be extended beyond the classical provisioning view, toward a more adequate capital consumption approach ("Impact of Risk"). On this basis, the difference between parameter-based EL and actually reported Impact of Risk is decomposed into its key components. The proposed method will deepen the understanding of the practical properties of EL, reconcile the EL with a clearly defined and observable risk measure and provide a link between upcoming IFRS 9 accounting standards for loan loss provisioning and the regulatory capital requirements under the internal ratings-based approach (IRBA). The method is robust irrespective of whether parameters are simple, expert-based values or highly predictive and perfectly calibrated IRBA-compliant methods, as long as the parameters and default identification procedures are stable.The attached pdf is aligned with respect to numbering of formulas, lemmas etc to the published version to simplify referencing. The terminology got slightly updated to provide a more consistent basis for further empirical research: PL/NPL Backtest is replaced by PL/NPL Dashboard.
