RATING BASED MODELING OF CREDIT RISK By Stefan Trueck & Svetlozar T. Rachev/NM

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LOOKS UNREAD: CLEAN AND TIGHT, LIGHT SCUFFING TO GLOSSY COVERS
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“LOOKS UNREAD: CLEAN AND TIGHT, LIGHT SCUFFING TO GLOSSY COVERS”
Book Title
Rating Based Modeling of Credit Risk: Theory and Application of
Item Height
9.02 inches
ISBN-10
0123736838
Item Weight
1.28 pounds
ISBN
9780123736833
Categoría

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Product Identifiers

Publisher
Elsevier Science & Technology
ISBN-10
0123736838
ISBN-13
9780123736833
eBay Product ID (ePID)
70023777

Product Key Features

Number of Pages
280 Pages
Publication Name
Rating Based Modeling of Credit Risk : Theory and Application of Migration Matrices
Language
English
Publication Year
2009
Subject
Personal Finance / Money Management, Banks & Banking, Finance / General, Decision-Making & Problem Solving
Type
Textbook
Author
Stefan Trueck, Svetlozar T. Rachev
Subject Area
Business & Economics
Series
Academic Press Advanced Finance Ser.
Format
Hardcover

Dimensions

Item Length
9 in
Item Width
6 in

Additional Product Features

Intended Audience
Scholarly & Professional
Reviews
"... an excellent overview of theory and application...." -Frank J. Fabozzi, PhD, CFA, Professor in the Practice of Finance, Yale School of Management, CT, "... an excellent overview of theory and application...." -- Frank J. Fabozzi, PhD, CFA, Professor in the Practice of Finance, Yale School of Management, CT, "... an excellent overview of theory and application...." --Frank J. Fabozzi, PhD, CFA, Professor in the Practice of Finance, Yale School of Management, CT
Dewey Edition
22
Illustrated
Yes
Dewey Decimal
332.7011
Table Of Content
1. Introduction: Credit Risk Modeling, Ratings and Migration Matrices 2. Rating and Scoring Techniques 3. The New Basel Capital Accord 4. Rating Based Modeling 5. Migration Matrices and the Markov Chain Approach 6. Stability of Credit Migrations 7. Measures for Comparison of Transition Matrices 8. Real World and Risk-Neutral Transition Matrices 9. Conditional Credit Migrations: Adjustments and Forecasts 10. Dependence Modeling and Credit Migrations 11. Credit Derivatives
Synopsis
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., 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, 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.

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