Practical Spreadsheet Risk Modeling for Management  book cover
1st Edition

Practical Spreadsheet Risk Modeling for Management

ISBN 9781439855522
Published September 8, 2011 by Chapman & Hall
284 Pages 190 B/W Illustrations

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Book Description

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

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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.