Practical Spreadsheet Risk Modeling for Management: 1st Edition (Hardback) book cover

Practical Spreadsheet Risk Modeling for Management

1st Edition

By Dale Lehman, Huybert Groenendaal, Greg Nolder

Chapman and Hall/CRC

284 pages | 190 B/W Illus.

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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.


  • 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.

Table of Contents

Conceptual Maps and Models

Introductory Case: Mobile Phone Service

First Steps: Visualization

Retirement Planning Example

Good Practices with Spreadsheet Model Construction

Errors in Spreadsheet Modeling

Conclusion: Best Practices

Basic Monte Carlo Simulation in Spreadsheets

Introductory Case: Retirement Planning

Risk and Uncertainty

Scenario Manager

Monte Carlo Simulation

Monte Carlo Simulation Using ModelRisk

Monte Carlo Simulation for Retirement Planning

Discrete Event Simulation

Modeling with Objects

Introductory Case: An Insurance Problem

Frequency and Severity


Using Objects in the Insurance Model

Modeling Frequency/Severity without Using Objects

Modeling Deductibles

Using Objects without Simulation

Multiple Severity/Frequency Distributions

Uncertainty and Variability

Selecting Distributions

First Introductory Case: Valuation of a Public Company—Using Expert Opinion

Modeling Expert Opinion in the Valuation Model

Second Introductory Case: Value at Risk—Fitting

Distributions to Data

Distribution Fitting for VaR, Parameter Uncertainty, and Model Uncertainty

Commonly Used Discrete Distributions

Commonly Used Continuous Distributions

A Decision Guide for Selecting Distributions

Bayesian Estimation

Modeling Relationships

First Example: Drug Development

Second Example: Collateralized Debt Obligations

Multiple Correlations

Third Example: How Correlated Are Home Prices?—Copulas

Empirical Copulas

Fourth Example: Advertising Effectiveness

Regression Modeling

Simulation within Regression Models

Multiple Regression Models

The Envelope Method


Time Series Models

Introductory Case: September 11 and Air Travel

The Need for Time Series Analysis: A Tale of Two Series

Analyzing the Air Traffic Data

Second Example: Stock Prices

Types of Time Series Models

Third Example: Oil Prices

Fourth Example: Home Prices and Multivariate Time Series.

Markov Chains

Optimization and Decision Making

Introductory Case: Airline Seat Pricing

A Simulation Model of the Airline Pricing Problem

A Simulation Table to Explore Pricing Strategies

An Optimization Solution to the Airline Pricing Problem

Optimization with Simulation

Optimization with Multiple Decision Variables

Adding Requirements

Presenting Results for Decision Making

Stochastic Dominance

Appendix A: Monte Carlo Simulation Software


A Brief Tour of Four Monte Carlo Packages


About the Authors

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 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.

Subject Categories

BISAC Subject Codes/Headings:
BUSINESS & ECONOMICS / Operations Research
MATHEMATICS / Probability & Statistics / General