Practical Spreadsheet Modeling Using @Risk

Dale E. Lehman, PhD, is Professor of Business Administration and Director of the EMBA in Business Analytics at Loras College. He has taught at numerous universities in North America, Europe, and Asia. He has also published extensively in the areas of microeconomics, with applications in the telecommunications, health care, and natural resource industries. He has authored three previous books in these areas. Huybert Groenendaal, PhD, is Managing Director at EpiX Analytics. He has extensive experience in using risk modeling to support decision making in fields that include business development, financial valuation, and R&D portfolio evaluation within the pharmaceutical and medical device industries, as well as health and epidemiology, energy, manufacturing and private equity. He regularly teaches risk analysis training classes.

Chapter 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 Chapter 1 Exercises. Chapter 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 Presenting Results for Decision Making 2.8 Discrete Event Simulation Chapter 2 Exercises. Chapter 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 VaR – Parameter and Model Uncertainty 3.4.1 Parameter Uncertainty 3.4.2 Model Uncertainty 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 Chapter 3 Exercises. Chapter 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 Chapter 4 Exercises. Chapter 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 Chapter 5 Exercises. Chapter 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 Chapter 6 Exercises. Chapter 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 An 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 Chapter 7 Exercises. Appendix: Risk Analysis in Projects