Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS (Wiley and SAS Business Series) - Hardcover

Über die Autorin bzw. den Autor

BART BAESENS is a professor at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom).

DANIEL RÖSCH is a professor in business and management and chair in statistics and risk management at the University of Regensburg (Germany).

HARALD SCHEULE is an associate professor of finance at the University of Technology Sydney (Australia) and a regional director of the Global Association of Risk Professionals.

Von der hinteren Coverseite

Credit risk analytics is undoubtedly one of the most crucial players in the field of financial risk management. With the recent financial downturn and the regulatory changes introduced by the Basel accords, credit risk analytics has been attracting greater attention from the banking and finance industries worldwide.

Now, risk professionals have an inclusive, targeted training guide to producing quality, standardized, and scalable in-house models for credit risk management. Credit Risk Analytics begins with a complete primer on SAS, including how to explicitly program and code the various data steps and models, extract information from data without having to rely on programming, compute basic statistics, and pre-process data. Whether you're building a model from scratch or validating an existing one, this single resource gives you all the insight and practical advice you need on such critical issues as regulatory requirements and stress-testing of credit risk models, including marginal loss given default (LGD) and exposure at default (EAD) models.

A state-of-the-art companion website expedites real-world implementation with clarifying examples of both actual and simulated credit portfolio data, as well as added practical guidance from the author team. This expert resource enables you to:

• Master the critical probability of default parameter of risk management, including converting credit scores and other information into default probabilities using discrete-time and continuous-time hazard models
• Estimate default and asset correlations and create loss distributions using analytical methods and Monte Carlo simulation
• Build on various models throughout the book with capstone modeling strategies, including Bayesian models

No other solutions package provides the depth of coverage and level of expertise on aligning risk management theory with the latest code. Keep Credit Risk Analytics at your fingertips for everything you need to analyze credit risk of loans and loan portfolios in the commercial banking industry.

Aus dem Klappentext

Credit risk analytics is undoubtedly one of the most crucial players in the field of financial risk management. With the recent financial downturn and the regulatory changes introduced by the Basel accords, credit risk analytics has been attracting greater attention from the banking and finance industries worldwide.

Now, risk professionals have an inclusive, targeted training guide to producing quality, standardized, and scalable in-house models for credit risk management. Credit Risk Analytics begins with a complete primer on SAS, including how to explicitly program and code the various data steps and models, extract information from data without having to rely on programming, compute basic statistics, and pre-process data. Whether you're building a model from scratch or validating an existing one, this single resource gives you all the insight and practical advice you need on such critical issues as regulatory requirements and stress-testing of credit risk models, including marginal loss given default (LGD) and exposure at default (EAD) models.

A state-of-the-art companion website expedites real-world implementation with clarifying examples of both actual and simulated credit portfolio data, as well as added practical guidance from the author team. This expert resource enables you to:

• Master the critical probability of default parameter of risk management, including converting credit scores and other information into default probabilities using discrete-time and continuous-time hazard models
• Estimate default and asset correlations and create loss distributions using analytical methods and Monte Carlo simulation
• Build on various models throughout the book with capstone modeling strategies, including Bayesian models

No other solutions package provides the depth of coverage and level of expertise on aligning risk management theory with the latest code. Keep Credit Risk Analytics at your fingertips for everything you need to analyze credit risk of loans and loan portfolios in the commercial banking industry.