Credit Risk Modeling using Excel and VBA 2ed

The second edition includes new coverage of the important issue of how parameter uncertainty can be dealt with in the estimation of portfolio risk, as well as comprehensive new sections on the pricing of CFSs and CDOs, and a chapter on predicting borrower-specific loss given default with regression models. In all, the authors present a host of applications - many of which go beyond standard Excel or VBA usages, for example, how to estimate logit models with maximum likelihood, or how to quickly conduct large-scale Monte Carlo simulations.

Clearly written with a multitude of practical examples, the new edition of "Credit Risk Modeling using Excel and VBA" will prove an indispensable resource for anyone working in, studying or researching this important field.

Praise for the first edition

""In one place, Loffler and Posch provide all that is needed to install a state-of-the-art risk management system, including a broad understanding of different risk management frameworks, detailed estimation techniques for dertiving PD, LGD, and correlation parameters, and programming tools for putting thesr methods into practice."" - Richard Cantor, Chief Credit Officer, Moody's Investor Service

The authors present a host of applications – many of which go beyond standard Excel or VBA usages. For example, they show how to estimate logit models with maximum likelihood, or how to conduct large–scale Monte Carlo simulations in little time. Even to experienced modelers the book can serve as a toolbox and source of inspiration.  

"In one place, Löffler and Posch provide all that is needed to install state–of–the–art risk management system, including a broad understanding of different risk management frameworks, detailed estimation techniques for deriving PD, LGD, and correlation parameters, and programing tools for putting these methods into practice."
—Richard Cantor, Managing Director, Credit Policy Research, Moody’s Investors Service

—Greg M. Gupton, Fitch Ratings & DefaultRisk.com --This text refers to an out of print or unavailable edition of this title.