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 fieldsDescription of probabilistic methods, their theoretical foundations, and their practical application in a spreadsheet environmentExtensive example models and exercises based on real data and relevant decision problemsComprehensive use of the @Risk software for simulation analysis, including a free one-year educational software license
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, this book dispenses concentrates on how powerful techniques and methods can be used correctly within a spreadsheet-based environment
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
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.
