Financial Modeling with Crystal Ball and Excel
Financial Modeling with Crystal Ball(r) and Excel(r)
"Professor Charnes's book drives clarity into applied Monte Carlo analysis using examples and tools relevant to real-world finance. The book will prove useful for analysts of all levels and as a supplement to academic courses in multiple disciplines."
-Mark Odermann, Senior Financial Analyst, Microsoft
"Think you really know financial modeling? This is a must-have for power Excel users. Professor Charnes shows how to make more realistic models that result in fewer surprises. Every analyst needs this credibility booster."
-James Franklin, CEO, Decisioneering, Inc.
"This book packs a first-year MBA's worth of financial and business modeling education into a few dozen easy-to-understand examples. Crystal Ball software does the housekeeping, so readers can concentrate on the business decision. A careful reader who works the examples on a computer will master the best general-purpose technology available for working with uncertainty."
-Aaron Brown, Executive Director, Morgan Stanley, author of The Poker Face of Wall Street
"Using Crystal Ball and Excel, John Charnes takes you step by step, demonstrating a conceptual framework that turns static Excel data and financial models into true risk models. I am astonished by the clarity of the text and the hands-on, step-by-step examples using Crystal Ball and Excel; Professor Charnes is a masterful teacher, and this is an absolute gem of a book for the new generation of analyst."
-Brian Watt, Chief Operating Officer, GECC, Inc.
"Financial Modeling with Crystal Ball and Excel is a comprehensive, well-written guide to one of the most useful analysis tools available to professional risk managers and quantitative analysts. This is a must-have book for anyone using Crystal Ball, and anyone wanting an overview of basic risk management concepts."
-Paul Dietz, Manager, Quantitative Analysis, Westar Energy
"John Charnes presents an insightful exploration of techniques for analysis and understanding of risk and uncertainty in business cases. By application of real options theory and Monte Carlo simulation to planning, doors are opened to analysis of what used to be impossible, such as modeling the value today of future project choices."
-Bruce Wallace, Nortel
About the Author.
Chapter 1: Introduction.
Monte Carlo Simulation.
Benefits and Limitations of Using Crystal Ball.
Chapter 2: Analyzing Crystal Ball Forecasts.
Simulating A 50–50 Portfolio.
Varying the Allocations.
Presenting the Results.
Chapter 3: Building a Crystal Ball Model.
Simulation Modeling Process.
Defining Crystal Ball Assumptions.
Running Crystal Ball.
Sources of Error.
Controlling Model Error.
Chapter 4: Selecting Crystal Ball Assumptions.
Crystal Ball’s Basic Distributions.
Using Historical Data to Choose Distributions.
Chapter 5: Using Decision Variables.
Defining Decision Variables.
Decision Table with One Decision Variable.
Decision Table with Two Decision Variables.
Chapter 6: Selecting Run Preferences.
Chapter 7: Net Present Value and Internal Rate of Return.
Deterministic NPV and IRR.
Simulating NPV and IRR.
Customer Net Present Value.
Chapter 8: Modeling Financial Statements.
Tornado Chart and Sensitivity Analysis.
Crystal Ball Sensitivity Chart.
Chapter 9: Portfolio Models.
Single-Period Crystal Ball Model.
Single-Period Analytical Solution.
Multiperiod Crystal Ball Model.
Chapter 10: Value at Risk.
Shortcomings of VaR.
Chapter 11: Simulating Financial Time Series.
Additive Random Walk with Drift.
Multiplicative Random Walk Model.
Geometric Brownian Motion Model.
Chapter 12: Financial Options.
Types of Options.
Risk-Neutral Pricing and the Black-Scholes Model.
American Option Pricing.
Exotic Option Pricing.
Chapter 13: Real Options.
Financial Options and Real Options.
Applications of ROA.
Black-Scholes Real Options Insights.
Appendix A: Crystal Ball’s Probability Distributions.
Appendix B: Generating Assumption Values.
Appendix C: Variance Reduction Techniques.
Appendix D: About the Download.
- After reviewing the basics, this book covers how to define and refine probability distributions in financial modeling, and exhaustively reviews the concepts behind the simulation modeling process.
- It includes a discussion of simulation controls and analysis of simulation results.
- Exercise models help students apply risk analysis to such areas as derivative pricing, cost estimation, portfolio allocation and optimization, design analysis, and cash flow analysis.
- The tools and techniques reviewed will help students immediately develop essential skills in the areas of areas of valuation, pricing, hedging, trading, risk management, project evaluation and portfolio management.