Rating Based Modeling of Credit Risk

Rating Based Modeling of Credit Risk
Author :
Publisher : Academic Press
Total Pages : 279
Release :
ISBN-10 : 9780080920306
ISBN-13 : 0080920306
Rating : 4/5 (306 Downloads)

Book Synopsis Rating Based Modeling of Credit Risk by : Stefan Trueck

Download or read book Rating Based Modeling of Credit Risk written by Stefan Trueck and published by Academic Press. This book was released on 2009-01-15 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade rating-based models have become very popular in credit risk management. These systems use the rating of a company as the decisive variable to evaluate the default risk of a bond or loan. The popularity is due to the straightforwardness of the approach, and to the upcoming new capital accord (Basel II), which allows banks to base their capital requirements on internal as well as external rating systems. Because of this, sophisticated credit risk models are being developed or demanded by banks to assess the risk of their credit portfolio better by recognizing the different underlying sources of risk. As a consequence, not only default probabilities for certain rating categories but also the probabilities of moving from one rating state to another are important issues in such models for risk management and pricing. It is widely accepted that rating migrations and default probabilities show significant variations through time due to macroeconomics conditions or the business cycle. These changes in migration behavior may have a substantial impact on the value-at-risk (VAR) of a credit portfolio or the prices of credit derivatives such as collateralized debt obligations (D+CDOs). In Rating Based Modeling of Credit Risk the authors develop a much more sophisticated analysis of migration behavior. Their contribution of more sophisticated techniques to measure and forecast changes in migration behavior as well as determining adequate estimators for transition matrices is a major contribution to rating based credit modeling. Internal ratings-based systems are widely used in banks to calculate their value-at-risk (VAR) in order to determine their capital requirements for loan and bond portfolios under Basel II One aspect of these ratings systems is credit migrations, addressed in a systematic and comprehensive way for the first time in this book The book is based on in-depth work by Trueck and Rachev


Rating Based Modeling of Credit Risk Related Books

Rating Based Modeling of Credit Risk
Language: en
Pages: 279
Authors: Stefan Trueck
Categories: Business & Economics
Type: BOOK - Published: 2009-01-15 - Publisher: Academic Press

DOWNLOAD EBOOK

In the last decade rating-based models have become very popular in credit risk management. These systems use the rating of a company as the decisive variable to
Accounting and Information Theory
Language: en
Pages: 106
Authors: Baruch Lev
Categories: Accounting
Type: BOOK - Published: 1969 - Publisher:

DOWNLOAD EBOOK

Financial Statement Analysis and the Prediction of Financial Distress
Language: en
Pages: 89
Authors: William H. Beaver
Categories: Business & Economics
Type: BOOK - Published: 2011 - Publisher: Now Publishers Inc

DOWNLOAD EBOOK

Financial Statement Analysis and the Prediction of Financial Distress discusses the evolution of three main streams within the financial distress prediction lit
Corporate Bankruptcy Prediction
Language: en
Pages: 202
Authors: Błażej Prusak
Categories: Business & Economics
Type: BOOK - Published: 2020-06-16 - Publisher: MDPI

DOWNLOAD EBOOK

Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the mark
Probabilistic Methods for Financial and Marketing Informatics
Language: en
Pages: 427
Authors: Richard E. Neapolitan
Categories: Mathematics
Type: BOOK - Published: 2010-07-26 - Publisher: Elsevier

DOWNLOAD EBOOK

Probabilistic Methods for Financial and Marketing Informatics aims to provide students with insights and a guide explaining how to apply probabilistic reasoning