Practical Spreadsheet Risk Modeling for Management

Risk analytics is developing rapidly, and analysts in the field need material that is theoretically sound as well as practical and straightforward. A one-stop resource for quantitative risk analysis, Practical Spreadsheet Risk Modeling for Management dispenses with the use of complex mathematics, concentrating on how powerful techniques and methods can be used correctly within a spreadsheet-based environment. Highlights Covers important topics for modern risk analysis, such as frequency-severity modeling and modeling of expert opinion Keeps mathematics to a minimum while covering fairly advanced topics through the use of powerful software tools Contains an unusually diverse selection of topics, including explicit treatment of frequency-severity modeling, copulas, parameter and model uncertainty, volatility modeling in time series, Markov chains, Bayesian modeling, stochastic dominance, and extended treatment of modeling expert opinion End-of-chapter exercises span eight application areas illustrating the broad application of risk analysis tools with the use of data from real-world examples and case studies This book is written for anyone interested in conducting applied risk analysis in business, engineering, environmental planning, public policy, medicine, or virtually any field amenable to spreadsheet modeling. The authors provide practical case studies along with detailed instruction and illustration of the features of ModelRisk®, the most advanced risk modeling spreadsheet software currently available. If you intend to use spreadsheets for decision-supporting analysis, rather than merely as placeholders for numbers, then this is the resource for you.

Conceptual Maps and ModelsIntroductory Case: Mobile Phone ServiceFirst Steps: VisualizationRetirement Planning ExampleGood Practices with Spreadsheet Model ConstructionErrors in Spreadsheet ModelingConclusion: Best PracticesBasic Monte Carlo Simulation in SpreadsheetsIntroductory Case: Retirement PlanningRisk and UncertaintyScenario ManagerMonte Carlo SimulationMonte Carlo Simulation Using ModelRiskMonte Carlo Simulation for Retirement PlanningDiscrete Event SimulationModeling with ObjectsIntroductory Case: An Insurance ProblemFrequency and SeverityObjectsUsing Objects in the Insurance ModelModeling Frequency/Severity without Using ObjectsModeling DeductiblesUsing Objects without SimulationMultiple Severity/Frequency DistributionsUncertainty and VariabilitySelecting DistributionsFirst Introductory Case: Valuation of a Public Company—Using Expert OpinionModeling Expert Opinion in the Valuation ModelSecond Introductory Case: Value at Risk—FittingDistributions to DataDistribution Fitting for VaR, Parameter Uncertainty, and Model UncertaintyCommonly Used Discrete DistributionsCommonly Used Continuous DistributionsA Decision Guide for Selecting DistributionsBayesian EstimationModeling RelationshipsFirst Example: Drug DevelopmentSecond Example: Collateralized Debt ObligationsMultiple CorrelationsThird Example: How Correlated Are Home Prices?—CopulasEmpirical CopulasFourth Example: Advertising EffectivenessRegression ModelingSimulation within Regression ModelsMultiple Regression ModelsThe Envelope MethodSummaryTime Series ModelsIntroductory Case: September 11 and Air TravelThe Need for Time Series Analysis: A Tale of Two SeriesAnalyzing the Air Traffic DataSecond Example: Stock PricesTypes of Time Series ModelsThird Example: Oil PricesFourth Example: Home Prices and Multivariate Time Series.Markov ChainsOptimization and Decision MakingIntroductory Case: Airline Seat PricingA Simulation Model of the Airline Pricing ProblemA Simulation Table to Explore Pricing StrategiesAn Optimization Solution to the Airline Pricing ProblemOptimization with SimulationOptimization with Multiple Decision VariablesAdding RequirementsPresenting Results for Decision MakingStochastic DominanceAppendix A: Monte Carlo Simulation SoftwareIntroductionA Brief Tour of Four Monte Carlo PackagesIndex

Dale Lehman is Professor of Economics and Director of the MBA Program at Alaska Pacific University. He also teaches courses at Danube University and the Vienna University of Technology. He has held positions at a dozen universities and for several telecommunications companies. He holds a B.A. in Economics from SUNY at Stony Brook and M.A. and Ph.D. degrees from the University of Rochester. He has authored numerous articles and two books on topics related to microeconomic theory, decision making under uncertainty, and public policy, particularly concerning telecommunications and natural resources. Huybert Groenendaal is a managing partner and senior risk analysis consultant at EpiX Analytics. As a consultant, he helps clients using risk analysis modeling techniques in a broad range of industries. He has extensive experience in risk modeling in business development, financial valuation, and R&D portfolio evaluation within the pharmaceutical and medical device industries, but also works regularly in a variety of other fields, including investment management, health and epidemiology, and inventory management. He also teaches a number of risk analysis training classes, gives guest lectures at a number of universities, and is adjunct professor at Colorado State University. He holds a M.Sc. and Ph.D. from Wageningen University and an MBA in Finance from the Wharton School of Business. Greg Nolder is VP of Applied Analytics at Denali Alaskan Federal Credit Union. The mission of the Applied Analytics Department is to promote and improve the application of analytical techniques for measuring and managing risks at Denali Alaskan as well as the greater credit union industry. Along with Huybert, Greg is also an instructor of risk analysis courses for Statistics.com. Prior to Denali Alaskan he has had a varied career including work with EpiX Analytics as a risk analysis consultant for clients from numerous industries, sales engineer, application engineer, test engineer, and air traffic controller. Greg received a M.S. in Operations Research from Southern Methodist University as well as a B.S. in Electrical Engineering and a B.S. in Aviation Technology, both from Purdue University.