{"product_id":"operational-risk-with-excel-and-vba-isbn-9780471478874","title":"Operational Risk with Excel and VBA","description":"\u003cb\u003eA valuable reference for understanding operational risk\u003c\/b\u003e \u003cp\u003e\u003ci\u003eOperational Risk with Excel and VBA\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eNigel Da Costa Lewis\u003c\/b\u003e (Stamford, CT) is president and CEO of StatMetrics, a quantitative research boutique. He received his PhD from Cambridge University.\u003c\/p\u003e  Preface.  \u003cp\u003eAcknowledgments.\u003c\/p\u003e \u003cp\u003eCHAPTER 1: Introduction to Operational Risk Management and Modeling.\u003c\/p\u003e \u003cp\u003eWhat is Operational Risk?\u003c\/p\u003e \u003cp\u003eThe Regulatory Environment.\u003c\/p\u003e \u003cp\u003eWhy a Statistical Approach to Operational Risk Management?\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 2: Random Variables, Risk indicators, and Probability.\u003c\/p\u003e \u003cp\u003eRandom Variables and Operational Risk Indicators.\u003c\/p\u003e \u003cp\u003eTypes of Random Variable.\u003c\/p\u003e \u003cp\u003eProbability.\u003c\/p\u003e \u003cp\u003eFrequency and Subjective Probability.\u003c\/p\u003e \u003cp\u003eProbability Functions.\u003c\/p\u003e \u003cp\u003eCase Studies.\u003c\/p\u003e \u003cp\u003eCase Study 2.1: Downtown Investment Bank.\u003c\/p\u003e \u003cp\u003eCase Study 2.2: Mr. Mondey’s OPVaR.\u003c\/p\u003e \u003cp\u003eCase Study 2.3: Risk in Software Development.\u003c\/p\u003e \u003cp\u003eUseful Excel Functions.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 3: Expectation, Covariance, Variance, and Correlation.\u003c\/p\u003e \u003cp\u003eExpected Value of a RandomVariable.\u003c\/p\u003e \u003cp\u003eVariance and Standard Deviation.\u003c\/p\u003e \u003cp\u003eCovariance and Correlation.\u003c\/p\u003e \u003cp\u003eSome Rules for Correlation, Variance, and Covariance.\u003c\/p\u003e \u003cp\u003eCase Studies.\u003c\/p\u003e \u003cp\u003eCase Study 3.1: Expected Time to Complete a Complex Transaction.\u003c\/p\u003e \u003cp\u003eCase Study 3.2: Operational Cost of System Down Time.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 4: Modeling Central Tendency and Variability of Operational Risk Indicators.\u003c\/p\u003e \u003cp\u003eEmpirical Measures of Central Tendency.\u003c\/p\u003e \u003cp\u003eMeasures of Variability.\u003c\/p\u003e \u003cp\u003eCase Studies.\u003c\/p\u003e \u003cp\u003eCase Study 4.1: Approximating Business Risk.\u003c\/p\u003e \u003cp\u003eExcel Functions.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 5: Measuring Skew and Fat Tails of Operational Risk Indicators.\u003c\/p\u003e \u003cp\u003eMeasuring Skew.\u003c\/p\u003e \u003cp\u003eMeasuring Fat Tails.\u003c\/p\u003e \u003cp\u003eReview of Excel and VBA Functions for Skew and Fat Tails.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 6: Statistical Testing of Operational Risk Parameters.\u003c\/p\u003e \u003cp\u003eObjective and Language of Statistical Hypothesis Testing.\u003c\/p\u003e \u003cp\u003eSteps Involved In Conducting a Hypothesis Test.\u003c\/p\u003e \u003cp\u003eConfidence Intervals.\u003c\/p\u003e \u003cp\u003eCase Study 6.1: Stephan’s Mistake.\u003c\/p\u003e \u003cp\u003eExcel Functions for Hypothesis Testing.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 7: Severity of Loss Probability Models.\u003c\/p\u003e \u003cp\u003eNormal Distribution.\u003c\/p\u003e \u003cp\u003eEstimation of Parameters.\u003c\/p\u003e \u003cp\u003eBeta Distribution.\u003c\/p\u003e \u003cp\u003eErlang Distribution.\u003c\/p\u003e \u003cp\u003eExponential Distribution.\u003c\/p\u003e \u003cp\u003eGamma Distribution.\u003c\/p\u003e \u003cp\u003eLognormal Distribution.\u003c\/p\u003e \u003cp\u003ePareto Distribution.\u003c\/p\u003e \u003cp\u003eWeibull Distribution.\u003c\/p\u003e \u003cp\u003eOther Probability Distributions.\u003c\/p\u003e \u003cp\u003eWhat Distribution Best Fits My Severity of Loss Data?\u003c\/p\u003e \u003cp\u003eCase Study 7.1: Modeling Severity of Loss Legal Liability Losses.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 8: Frequency of Loss Probability Models.\u003c\/p\u003e \u003cp\u003ePopular Frequency of Loss Probability Models.\u003c\/p\u003e \u003cp\u003eOther Frequency of Loss Distributions.\u003c\/p\u003e \u003cp\u003eChi-Squared Goodness of Fit Test.\u003c\/p\u003e \u003cp\u003eCase Study 8.1: Key Personnel Risk.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 9: Modeling Aggregate Loss Distributions.\u003c\/p\u003e \u003cp\u003eAggregating Severity of Loss and Frequency of Loss Distributions.\u003c\/p\u003e \u003cp\u003eCalculating OpVaR.\u003c\/p\u003e \u003cp\u003eCoherent Risk Measures.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 10: The Law of Significant Digits and Fraud Risk Identification.\u003c\/p\u003e \u003cp\u003eThe Law of Significant Digits.\u003c\/p\u003e \u003cp\u003eBenford’s Law in Finance.\u003c\/p\u003e \u003cp\u003eCase Study 10.1: Analysis of Trader’s Profit and Loss Using Benford’s Law.\u003c\/p\u003e \u003cp\u003eA Step Towards Better Statistical Methods of Fraud Detection.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 11: Correlation and Dependence.\u003c\/p\u003e \u003cp\u003eMeasuring Correlation.\u003c\/p\u003e \u003cp\u003eDependence.\u003c\/p\u003e \u003cp\u003eStochastic Dependence.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 12: Linear Regression in Operational Risk Management.\u003c\/p\u003e \u003cp\u003eThe Simple Linear Regression Model.\u003c\/p\u003e \u003cp\u003eMultiple Regression.\u003c\/p\u003e \u003cp\u003ePrediction.\u003c\/p\u003e \u003cp\u003ePolynomial and Other Types of Regression.\u003c\/p\u003e \u003cp\u003eMultivariate Multiple Regression.\u003c\/p\u003e \u003cp\u003eRegime-Switching Regression.\u003c\/p\u003e \u003cp\u003eThe Difference Between Correlation and Regression.\u003c\/p\u003e \u003cp\u003eA Strategy for Regression Model Building in Operational Risk Management.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 13: Logistic Regression in Operational Risk Management.\u003c\/p\u003e \u003cp\u003eBinary Logistic Regression.\u003c\/p\u003e \u003cp\u003eBivariate Logistic Regression.\u003c\/p\u003e \u003cp\u003eCase Study 13.1: Nostro Breaks and Volume in a Bivariate Logistic Regression.\u003c\/p\u003e \u003cp\u003eOther Approaches for Modeling Bivariate Binary Endpoints.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 14: Mixed Dependent Variable Modeling.\u003c\/p\u003e \u003cp\u003eA Model for Mixed Dependent Variables.\u003c\/p\u003e \u003cp\u003eWorking Assumption of Independence.\u003c\/p\u003e \u003cp\u003eUnderstanding the Benefits of Using a WAI.\u003c\/p\u003e \u003cp\u003eCase Study 14.1: Modeling Failure in Compliance.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 15: Validating Operational Risk Proxies Using Surrogate Endpoints.\u003c\/p\u003e \u003cp\u003eThe Need for Surrogate Endpoints in OR Modeling.\u003c\/p\u003e \u003cp\u003eThe Prentice Criterion.\u003c\/p\u003e \u003cp\u003eLimitations of the Prentice Criterion.\u003c\/p\u003e \u003cp\u003eThe Real Value Added of Using Surrogate Variables.\u003c\/p\u003e \u003cp\u003eValidation Via the Proportion Explained.\u003c\/p\u003e \u003cp\u003eLimitations of Surrogate Modelling in Operational Risk Management.\u003c\/p\u003e \u003cp\u003eCase Study 15.1: Legal Experience as a Surrogate Endpoint for Legal Costs for a Business Unit.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 16: Introduction to Extreme Value Theory.\u003c\/p\u003e \u003cp\u003eFisher-Tippet–Gnedenko Theorem.\u003c\/p\u003e \u003cp\u003eMethod of Block Maxima.\u003c\/p\u003e \u003cp\u003ePeaks over Threshold Modeling.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 17: Managing Operational Risk with Bayesian Belief Networks.\u003c\/p\u003e \u003cp\u003eWhat is a Bayesian Belief Network?\u003c\/p\u003e \u003cp\u003eCase Study 17.1: A BBN Model for Software Product Risk.\u003c\/p\u003e \u003cp\u003eCreating a BBN-Based Simulation.\u003c\/p\u003e \u003cp\u003eAssessing the Impact of Different Managerial Strategies.\u003c\/p\u003e \u003cp\u003ePerceived Benefits of Bayesian Belief Network Modeling.\u003c\/p\u003e \u003cp\u003eCommon Myths About BBNs—The Truth for Operational Risk Management.\u003c\/p\u003e \u003cp\u003eSummary.\u003c\/p\u003e \u003cp\u003eReview Questions.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eCHAPTER 18: Epilogue.\u003c\/p\u003e \u003cp\u003eWinning the Operational Risk Argument.\u003c\/p\u003e \u003cp\u003eFinal Tips on Applied Operational Risk Modeling.\u003c\/p\u003e \u003cp\u003eFurther Reading.\u003c\/p\u003e \u003cp\u003eAppendix.\u003c\/p\u003e \u003cp\u003eStatistical Tables.\u003c\/p\u003e \u003cp\u003eCumulative Distribution Function of the Standard Normal Distribution.\u003c\/p\u003e \u003cp\u003eChi-Squared Distribution.\u003c\/p\u003e \u003cp\u003eStudent’s \u003ci\u003et\u003c\/i\u003e Distribution.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eF\u003c\/i\u003e Distribution.\u003c\/p\u003e \u003cp\u003eNotes.\u003c\/p\u003e \u003cp\u003eBibliography.\u003c\/p\u003e \u003cp\u003eAbout the CD-ROM.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e  \u003cb\u003eNIGEL DA COSTA LEWIS\u003c\/b\u003e, 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 \u0026amp; 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.  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.  \u003cp\u003eWritten by quantitative risk expert Nigel Da Costa Lewis, \u003ci\u003eOperational Risk with Excel and VBA\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eBased 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.\u003c\/p\u003e \u003cp\u003eOther issues covered include:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eRandom variables, risk indicators, and probability\u003c\/li\u003e \u003cli\u003eExpectation, covariance, variance, and correlation\u003c\/li\u003e \u003cli\u003eModeling central tendency and variability of operational risk indicators\u003c\/li\u003e \u003cli\u003eStatistical testing of operational risk parameters\u003c\/li\u003e \u003cli\u003eThe law of significant digits and fraud risk identification\u003c\/li\u003e \u003cli\u003eLinear regression in operational risk management\u003c\/li\u003e \u003cli\u003eValidating operational risk proxies using surrogate endpoints\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eTo improve your understanding of the methods discussed, case studies, examples, and review questions are also included in many chapters.\u003c\/p\u003e \u003cp\u003eWhether you're a financial professional, consultant, or academic, \u003ci\u003eOperational Risk with Excel and VBA\u003c\/i\u003e 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.\u003c\/p\u003e  \u003cb\u003ePraise For \u003ci\u003eOperational Risk with Excel and VBA\u003c\/i\u003e\u003c\/b\u003e  \u003cp\u003e\"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.\"\u003cbr\u003e —\u003cb\u003eDr. O.F. Agbaje\u003c\/b\u003e, School of Informatics, City University, London\u003c\/p\u003e \u003cp\u003e\"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.\"\u003cbr\u003e —\u003cb\u003eProf. Bernard Beecher\u003c\/b\u003e, Department of Mathematics, City University, New York\u003c\/p\u003e \u003cp\u003e\"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.\"\u003cbr\u003e —\u003cb\u003eDr. Terence Yiu Wa Chow\u003c\/b\u003e, Department of Mathematics, University College London, University of London\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989718843621,"sku":"NP9780471478874","price":94.5,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780471478874.jpg?v=1761785234","url":"https:\/\/k12savings.com\/products\/operational-risk-with-excel-and-vba-isbn-9780471478874","provider":"K12savings","version":"1.0","type":"link"}