Practical Spreadsheet Modeling Using @Risk

Description

Practical Spreadsheet Modeling Using @Risk provides a guide of how to construct applied decision analysis models in spreadsheets. The focus is on the use of Monte Carlo simulation to provide quantitative assessment of uncertainties and key risk drivers. The book presents numerous examples based on real data and relevant practical decisions in a variety of settings, including health care, transportation, finance, natural resources, technology, manufacturing, retail, and sports and entertainment. All examples involve decision problems where uncertainties make simulation modeling useful to obtain decision insights and explore alternative choices. Good spreadsheet modeling practices are highlighted. The book is suitable for graduate students or advanced undergraduates in business, public policy, health care administration, or any field amenable to simulation modeling of decision problems. The book is also useful for applied practitioners seeking to build or enhance their spreadsheet modeling skills. Features Step-by-step examples of spreadsheet modeling and risk analysis in a variety of fields Description of probabilistic methods, their theoretical foundations, and their practical application in a spreadsheet environment Extensive example models and exercises based on real data and relevant decision problems Comprehensive use of the @Risk software for simulation analysis, including a free one-year educational software license Cover Half Title Title Page Copyright Page Dedication Contents Preface Acknowledgments Authors Introduction 1. Conceptual Maps and Models 1.1 Introductory Case MoviePass 1.2 First Steps: Visualization 1.3 Retirement Planning Example 1.4 Good Practices with Spreadsheet Model Construction 1.5 Errors in Spreadsheet Modeling 1.6 Decision Analysis 1.7 Conclusion: Best Practices Exercises Notes 2. Basic Monte Carlo Simulation in Spreadsheets 2.1 Introductory Case: Retirement Planning 2.2 Risk and Uncertainty 2.3 Scenario Manager 2.4 Monte Carlo Simulation 2.4.1 Generating Random Numbers 2.4.2 Monte Carlo Simulation for MoviePass 2.5 Monte Carlo Simulation Using @Risk 2.6 Monte Carlo Simulation for Retirement Planning 2.7 Discrete Event Simulation Exercises Notes 3. Selecting Distributions 3.1 First Introductory Case: Valuation of a Public Company Using Expert Opinion 3.2 Modeling Expert Opinion in the Valuation Model 3.3 Second Introductory Case: Value at Risk—Fitting Distributions to Data 3.4 Distribution Fitting for Value at Risk—Parameter and Model Uncertainty 3.4.1 Parameter Uncertainty (More Advanced, Optional) 3.4.2 Model Uncertainty (Most Advanced, Optional) 3.5 Third Introductory Case: Failure Distributions 3.6 Commonly Used Discrete Distributions 3.7 Commonly Used Continuous Distributions 3.8 A Brief Decision Guide for Selecting Distributions Exercises Notes 4. Modeling Relationships 4.1 First Example: Drug Development 4.2 Second Example: Collateralized Debt Obligations 4.3 Multiple Correlations Example: Cockpit Failures 4.4 Copulas Example: How Correlated Are Home Prices? 4.5 Empirical Copulas 4.6 Fifth Example: Advertising Effectiveness 4.7 Regression Modeling 4.8 Simulation within Regression Models 4.9 Multiple Linear Regression Models 4.10 The Envelope Method 4.11 Summary Exercises Notes 5. Time Series Models 5.1 The Need for Time Series Analysis: A Tale of Two Series 5.2 Introductory Case: Air Travel and September 11 5.3 Analyzing the Air Traffic Data and 9/11 5.4 Second Example: Stock Prices 5.5 Types of Time Series Models 5.6 Third Example: Soybean Prices 5.7 Fourth Example: Home Prices and Multivariate Time Series Exercises Notes 6. Additional Useful Techniques 6.1 Advanced Sensitivity Analysis 6.2 Stress Testing 6.3 Non-Parametric Distributions 6.4 Case: An Insurance Problem 6.5 Frequency and Severity 6.6 The Compound Distribution 6.7 Uncertainty and Variability 6.8 Bayesian Analysis Exercises Notes 7. Optimization and Decision Making 7.1 Introductory Case: Airline Seat Pricing 7.2 A Simulation Model of the Airline Pricing Problem 7.3 A Simulation Table to Explore Pricing Strategies 7.4 A Stochastic Optimization Solution to the Airline Pricing Problem 7.5 Optimization with Multiple Decision Variables 7.6 Adding Constraints 7.7 Efficient Frontier 7.8 Stochastic Dominance 7.9 Summary Exercises Notes Appendix: Risk Analysis in Projects Index