[Free ebook]Operational Risk with Excel and VBA: Applied Statistical Methods for Risk Management, + Website-NIGEL DA COSTA LEWIS - KING OF EXCEL

Saturday, February 22, 2020

[Free ebook]Operational Risk with Excel and VBA: Applied Statistical Methods for Risk Management, + Website-NIGEL DA COSTA LEWIS

[Free ebook]Operational Risk with Excel and VBA: Applied Statistical Methods for Risk Management, + Website-NIGEL DA COSTA LEWIS



Product description

A valuable reference for understanding operational risk
Operational Risk with Excel and VBA is a practical guide that only discusses statistical methods that have been shown to work in an operational risk management context. It brings together a wide variety of statistical methods and models that have proven their worth, and contains a concise treatment of the topic. This book provides readers with clear explanations, relevant information, and comprehensive examples of statistical methods for operational risk management in the real world.
Nigel Da Costa Lewis (Stamford, CT) is president and CEO of StatMetrics, a quantitative research boutique. He received his PhD from Cambridge University.

From the Inside Flap

Understanding and managing operational risk is essential to a company'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.
Written by quantitative risk expert Nigel Da Costa Lewis, Operational Risk with Excel and VBA brings together a wide variety of statistical methods and models that have proven their worth in real-world business situations, and illustrates how these methods can improve a firm's overall management of operational risk events.
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 website 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.
Whether you're a financial professional, consultant, or academic, Operational Risk with Excel and VBA provides you with an authoritative and up-to-date treatment of the most crucial innovations in the application of statistical methods to operational risk modeling.

From the Back Cover

Praise For Operational Risk with Excel and VBA
"Dr. Nigel Da Costa Lewis has produced the most exciting volume ever on operational risk modeling. It is a must for students, practitioners, risk managers, and senior executives. . . I feel this work is the first step in revolutionizing the discipline. Other books in this field tell you in theory, there is little difference between theory and practice. Dr. Lewis’s work tells you what we all know, in practice, there is."
Dr. O.F. Agbaje, School of Informatics, City University, London
"Dr. Nigel Da Costa Lewis has raised the bar for books on operational risk. His book provides a bridge from the theoretical to the practical, and clears the fog between the buzzwords of operational risk management and the realities of useful modeling tools. The inclusion of numerous concrete examples and solutions will make a broad range of modeling techniques accessible to students of management science. Without a doubt, Dr. Lewis has put quality meat on the bare bones of this important management discipline. He is to be applauded for this magnificent effort."
Prof. Bernard Beecher, Department of Mathematics, City University, New York
"Dr. Nigel Da Costa Lewis has produced one of the most exciting and classic reference volumes on operational risk. As only great teachers can, Dr. Lewis makes even the most obtuse mathematics seem easy and intuitive . . . This book is a must for students, practitioners, and anybody interested in this important subject. In short, it is the most comprehensive and up-to-date textbook on operational risk modeling that I have seen."
Dr. Terence Yiu Wa Chow, Department of Mathematics, University College London, University of London

About the Author

NIGEL DA COSTA LEWIS, PHD, is the President of the quantitative research boutique StatMetrics, offering cutting edge quantitative solutions to a sophisticated institutional client base. Dr. Lewis has many years’ work experience as a quantitative analyst and statistician in London, on Wall Street, and in academia. His work in quantitative risk management dates back to the early 1990s, when he developed stress-testing methodologies for portfolios of derivative securities for Legal & General Investments. He is the author of a number of books on risk management and quantitative methods and a regular speaker at international conferences. His current research work specializes in the application of computational-intensive quantitative methods to problems in risk management. He received a PhD in statistics from the University of Cambridge, and master’s degrees in statistics, finance, economics, and computer science, all from the University of London.

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