Understanding and managing operational risk is essential to a companyÂ’s survival and prosperity. With the regulatory spotlight on operational risk management, there has been ever-increasing attention devoted to the quantification of operational risks. As a result, we have seen the emergence of a wide array of statistical methods for measuring, modeling, and monitoring operational risk.

Based on the author’s extensive experience, this book maps out the state-of-the-art statistical techniques that can be used to model operational risk. You’ll be introduced to the most important topics in this area, such as Bayesian Belief Networks, extreme value theory, and fitting frequency and severity of loss distributions to data. As you work your way through this concise guide, you’ll quickly learn how to model operational risk using tools such as Microsoft Excel and Visual Basic for Applications (VBA). Through the companion CD-ROM that supplements this book, you’ll be able to watch interactive illustrations of the modeling methods discussed and utilize a variety of Excel spreadsheets for your own endeavors in this field.

Other issues covered include:

- Random variables, risk indicators, and probability
- Expectation, covariance, variance, and correlation
- Modeling central tendency and variability of operational risk indicators
- Statistical testing of operational risk parameters
- The law of significant digits and fraud risk identification
- Linear regression in operational risk management
- Validating operational risk proxies using surrogate endpoints
- To improve your understanding of the methods discussed, case studies, examples, and review questions are also included in many chapters.

Operational Risk with Excel and VBA provides an authoritative and up-to-date treatment of the most crucial innovations in the application of statistical methods to operational risk modeling.