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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.

This book offers a one-stop resource for performing quantitative risk analyses. 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. The specific examples in the text demonstrate a number of cutting-edge tools and techniques that are very powerful in risk analysis but are not available in other spreadsheet simulation programs. The book covers modeling complex correlations, aggregating uncertainty and variability, and estimating parameter and model uncertainty.

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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

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