Financial Modeling with Crystal Ball and Excel E-Book

John Charnes

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Finance Editions
• Sprache: Englisch

Seitenzahl: 382

Veröffentlichungsjahr: 2012

Beschreibung

Updated look at financial modeling and Monte Carlo simulation with software by Oracle Crystal Ball This revised and updated edition of the bestselling book on financial modeling provides the tools and techniques needed to perform spreadsheet simulation. It answers the essential question of why risk analysis is vital to the decision-making process, for any problem posed in finance and investment. This reliable resource reviews the basics and covers how to define and refine probability distributions in financial modeling, and explores the concepts driving the simulation modeling process. It also discusses simulation controls and analysis of simulation results. The second edition of Financial Modeling with Crystal Ball and Excel contains instructions, theory, and practical example models to help apply risk analysis to such areas as derivative pricing, cost estimation, portfolio allocation and optimization, credit risk, and cash flow analysis. It includes the resources needed to develop essential skills in the areas of valuation, pricing, hedging, trading, risk management, project evaluation, credit risk, and portfolio management. * Offers an updated edition of the bestselling book covering the newest version of Oracle Crystal Ball * Contains valuable insights on Monte Carlo simulation--an essential skill applied by many corporate finance and investment professionals * Written by John Charnes, the former finance department chair at the University of Kansas and senior vice president of global portfolio strategies at Bank of America, who is currently President and Chief Data Scientist at Syntelli Solutions, Inc. Risk Analytics and Predictive Intelligence Division (Syntelli RAPID) Engaging and informative, this book is a vital resource designed to help you become more adept at financial modeling and simulation.

Leseprobe

Contents

Cover

Series

Title Page

Copyright

Preface

ORGANIZATION OF THIS BOOK

Acknowledgments

About the Author

Chapter 1: Introduction Introduction Financial Modeling with Crystal Ball and Excel

1.1 FINANCIAL MODELING

1.2 RISK ANALYSIS

1.3 MONTE CARLO SIMULATION

1.4 RISK MANAGEMENT

1.5 BENEFITS AND LIMITATIONS OF USING CRYSTAL BALL

Chapter 2: Analyzing Crystal Ball Forecasts

2.1 SIMULATING A 50–50 PORTFOLIO

2.2 VARYING THE ALLOCATIONS

2.3 PRESENTING THE RESULTS

Chapter 3: Building A Crystal Ball Model Building A Crystal Ball Model Financial Modeling with Crystal Ball and Excel

3.1 SIMULATION MODELING PROCESS

3.2 DEFINING CRYSTAL BALL ASSUMPTIONS AND FORECASTS

3.3 RUNNING CRYSTAL BALL

3.4 SOURCES OF ERROR

3.5 CONTROLLING MODEL ERROR

Chapter 4: Selecting Crystal Ball Assumptions

4.1 CRYSTAL BALL’S BASIC DISTRIBUTIONS

4.2 USING HISTORICAL DATA TO CHOOSE DISTRIBUTIONS

4.3 SPECIFYING CORRELATIONS

Chapter 5: Using Decision Variables

5.1 DEFINING DECISION VARIABLES

5.2 DECISION TABLE WITH ONE DECISION VARIABLE

5.3 DECISION TABLE WITH TWO DECISION VARIABLES

5.4 USING OPTQUEST

Chapter 6: Selecting Run Preferences

6.1 TRIALS

6.2 SAMPLING

6.3 SPEED

6.4 OPTIONS

6.5 STATISTICS

Chapter 7: Net Present Value and Internal Rate of Return

7.1 DETERMINISTIC NPV AND IRR

7.2 SIMULATING NPV AND IRR

7.3 CAPITAL BUDGETING

7.4 CUSTOMER NET PRESENT VALUE

Chapter 8: Modeling Financial Statements

8.1 DETERMINISTIC MODEL

8.2 TORNADO CHART AND SENSITIVITY ANALYSIS

8.3 CRYSTAL BALL SENSITIVITY CHART

8.4 CONCLUSION

Chapter 9: Portfolio Models

9.1 SINGLE-PERIOD CRYSTAL BALL MODEL

9.2 SINGLE-PERIOD ANALYTICAL SOLUTION

9.3 MULTI-PERIOD CRYSTAL BALL MODEL

Chapter 10: Value at Risk

10.1 VAR

10.2 SHORTCOMINGS OF VAR

10.3 CONDITIONAL VALUE AT RISK

Chapter 11: Simulating Financial Time Series

11.1 WHITE NOISE

11.2 RANDOM WALK

11.3 AUTOCORRELATION

11.4 ADDITIVE RANDOM WALK WITH DRIFT

11.5 MULTIPLICATIVE RANDOM WALK MODEL

11.6 GEOMETRIC BROWNIAN MOTION MODEL

11.7 MEAN-REVERTING MODEL

Chapter 12: Financial Options Financial Options Financial Modeling with Crystal Ball and Excel

12.1 TYPES OF OPTIONS

12.2 RISK-NEUTRAL PRICING AND THE BLACK-SCHOLES MODEL

12.3 PORTFOLIO INSURANCE

12.4 AMERICAN OPTION PRICING

12.5 EXOTIC OPTION PRICING

12.8 BULL SPREAD

12.7 PRINCIPAL-PROTECTED INSTRUMENT

Chapter 13: Real Options

13.1 FINANCIAL OPTIONS AND REAL OPTIONS

13.2 APPLICATIONS OF REAL OPTIONS ANALYSIS

13.3 BLACK-SCHOLES REAL OPTIONS INSIGHTS

13.4 REAL OPTIONS VALUATION TOOL

Chapter 14: Credit Risk

14.1 EXPECTED LOSS

14.2 CREDIT RISK SIMULATION MODEL

14.3 CONDITIONAL VALUE AT RISK

14.4 USING CVAR TO MANAGE CREDIT RISK

Chapter 15: Construction Project Management

15.1 PROJECT DESCRIPTION

15.2 CHOOSING CONSTRUCTION METHODS

15.3 RISK ANALYSIS

15.4 STOCHASTIC OPTIMIZATION

Chapter 16: Oil and Gas Exploration

16.1 WELL PROPERTIES

16.2 STATISTICAL MODELS

16.3 CONCLUSION

Appendix A: Crystal Ball’s Probability Distributions

A.1 BERNOULLI

A.2 BETA

A.3 BETA PERT

A.4 BINOMIAL

A.5 CUSTOM

A.6 DISCRETE UNIFORM

A.7 EXPONENTIAL

A.8 GAMMA

A.9 GEOMETRIC

A.10 HYPERGEOMETRIC

A.11 LOGISTIC

A.12 LOGNORMAL

A.13 MAXIMUM EXTREME

A.14 MINIMUM EXTREME

A.15 NEGATIVE BINOMIAL

A.16 NORMAL

A.17 PARETO

A.18 POISSON

A.19 STUDENT’S T

A.20 TRIANGULAR

A.21 UNIFORM

A.22 WEIBULL

A.23 YES-NO

Appendix B: Generating Assumption Values

B.1 GENERATING RANDOM NUMBERS

B.2 GENERATING RANDOM VARIATES

B.3 LATIN HYPERCUBE SAMPLING

Appendix C: Variance Reduction Techniques

C.1 USING CRYSTAL BALL TO VALUE AN ASIAN OPTION

C.2 ANTITHETIC VARIATES

C.3 CONTROL VARIATES

C.4 COMPARISON

C.5 CONCLUSION

Appendix D: About the Download

Trialware

Glossary

References

Index

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Copyright © 2012 by John Charnes. All rights reserved.

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Preface

I wrote this book to help statisticians, financial analysts, and other interested parties learn how to build and interpret the results of Crystal Ball® models for decision support. There are several books that exist to inform readers about Monte Carlo simulation in general. Many of these general books are listed in the References section of this book. This book focuses on using Crystal Ball in three main areas: corporate finance, investments, and derivatives. It also contains a chapter on using Crystal Ball in project management.

In 1982, University of Minnesota—Duluth Business School professor Henry Person introduced me to IFPS, computer software designed for financial planning, which we ran on VAX mainframe computers for an MBA class in quantitative methods. IFPS used a tabular layout for financial data similar to that used today by Excel. However, it was more abstract than Excel’s layout because one had to print the data to see the layout in IFPS instead of working with Excel’s tabular display of the data on the screen. Gray (1996) describes what is evidently the latest, and perhaps final, version of this financial planning software. It is significant to me because IFPS included a Monte Carlo command that gave me my first glimpse of using a computer as a financial risk analytic tool.

I was hooked. The next term, I took Henry’s class in discrete-event simulation based on Tom Schriber’s (1974) red GPSS textbook. I found the notion of discrete-event simulation fascinating. It made experimentation possible in a computer lab on models of real-world situations, just as the physical scale models of dams in the University of Minnesota—Twin Cities hydraulic laboratory made experimentation possible for the civil engineering researchers during my days as an undergraduate student there. I saw many places where systems simulation could have been applied to the construction industry when I worked as a field engineer, but was unaware at the time of what simulation could accomplish.

More graduate school beckoned. After a year of teaching finance at the University of Washington in Seattle, I returned to the Twin Cities to eventually earn my doctorate in what became the Carlson School of Management. There I met David Kelton in 1986. His coauthored textbook, Law and Kelton (2000), got me started on my dissertation research that was done largely at the Minnesota Supercomputer Institute, where I ran FORTRAN programs on Cray supercomputers and graphed the resulting output on Sun workstations. Today it is possible to do the same tasks faster and more easily by using Crystal Ball on a personal computer. I wish that I had had today’s version of the personal computer and Crystal Ball available to me when I worked as an economic analyst in 1985 at the organization that is now part of U.S. Bank.

As an assistant professor in the management sciences department at the University of Miami in Coral Gables, Florida, I taught simulation to systems analysis and industrial engineering students in their undergraduate and graduate programs. When I moved to the University of Kansas (KU) in 1994, I had hopes of offering a similar course of study, but learned quickly that the business students there were more interested in financial risk analysis than systems simulation. In 1996, I offered my first course in risk analysis at the KU suburban Kansas City campus to 30 MBA students, who loved the material but not the software we used—which was neither IFPS nor Crystal Ball.

I heard many complaints that term about the “clunky software that crashed all the time,” but one student posed an alternative. She asked if I had heard of Crystal Ball, which was then in use by a couple of her associates at Sprint, the Kansas City–based telecommunications company. I checked it out, and the more I read in the Crystal Ball documentation, the more convinced I became that the authors were influenced by the same Law and Kelton text that I had studied in graduate school.

At the 1997 Winter Simulation Conference, I met Eric Wainwright, chief technical officer at Decisioneering, Inc. (DI), and one of the two creators of Crystal Ball, who confirmed my suspicions about our shared background. Thus began my friendship with DI that led to creation of Risk Analysis Using Crystal Ball, the multimedia training CD-ROM offered on the DI web site. That effort, in collaboration with Larry Goldman, Lucie Trepanier, and Dave Fredericks, was a wholly enjoyable experience that gave me reason to believe—correctly—that the effort to produce this book would also be enjoyable.

About the same time I met Eric, I had the good fortune to work with David Kellogg at Sprint. His interest in Crystal Ball and invitation to present a series of lectures on its use as a decision-support tool led to my development of training classes that were part of the Sprint University of Excellence offerings for several years. I am grateful to David and all the participants in those classes over the years for helping me to hone the presentation of the ideas contained in this book. I am also grateful to Sprint and Nortel Networks for the financial support that led to the development of the real options valuation tool described in Chapter 13. Other consulting clients will go unnamed here, but they also have influenced the presentation.

Microsoft Excel has become the lingua franca of business. Business associates in different industries and even some in different divisions of the same company often find it difficult to communicate with each other. However, virtually everyone who does business planning uses Excel in some capacity, if not exclusively. Though not always able to communicate in the same language, businesspeople around the globe are able to share their Excel spreadsheets. As with everything in our society, Excel has its critics. Yet the overwhelming number of users of this program make it likely to be with us for a long time to come.

My main criticism of Excel is obviated by use of the Crystal Ball application. Excel is extremely versatile in its ability to allow one to build deterministic models in many different business, engineering, and scientific domains. Without Crystal Ball, it is cumbersome to use Excel for stochastic modeling, but Crystal Ball’s graphical input and output features make it easy for analysts to build stochastic models in Excel.

In the 1970s, Jerry Wagner and the other founders of IFPS had a dream of creating software that would dominate the market for a computerized, plain-language tool for financial planning by executives. In the meantime, Microsoft Excel came to dominate the market for financial planning software. The combination of Excel, Crystal Ball, and OptQuest provides a powerful way for you to enhance your deterministic models by adding stochastic assumptions and finding optimal solutions to complex real-world problems. Building such models will give you greater insight into the problems you face, and may cause you to view your business in a new light.

ORGANIZATION OF THIS BOOK

This book is intended for analysts who wish to construct stochastic financial models, and anyone else interested in learning how to use Crystal Ball. Instructors with a practical bent may also find it useful as a supplement for courses in finance, statistics, management science, or industrial engineering.

The first six chapters of this book cover the features of Crystal Ball and OptQuest. Several examples are used to illustrate how these programs can be used to enhance deterministic Excel models for stochastic financial analysis and planning. The remaining ten chapters provide more detailed examples of how Crystal Ball and OptQuest can be used in financial risk analysis of investments in securities, derivatives, real options, and project management. The technical appendices provide details about the methods used by Crystal Ball in its algorithms, and a description of some methods of variance reduction that can be employed to increase the precision of your simulation estimates. All of the models described in the book are available through a link to a web site from which a trial version of Crystal Ball may also be downloaded. The contents of each chapter and appendix are listed below:

Chapter 1 provides an overview of financial modeling and risk analysis through Monte Carlo simulation. It also contains a discussion of risk management and the benefits and limitations of Crystal Ball.

Chapter 2 describes how to specify and interpret Crystal Ball forecasts, the graphical and numerical summaries of the output measures generated during simulation. A retirement portfolio is used for an example.

Chapter 3 takes a helicopter view of building a Crystal Ball model. It starts out with a simple, deterministic business planning Excel model, and then shows you how to add stochastic assumptions to it with Crystal Ball. The chapter also contains a discussion of possible sources of error in your models and how they can be controlled.

Chapter 4 contains a deeper look at specifying Crystal Ball assumptions. It describes Crystal Ball’s basic distributions and shows you how to select distributions using historical data and/or your best expert judgment. The chapter also describes how to use, estimate, and specify correlations between assumptions in a Crystal Ball model.

Chapter 5 covers the use of decision variables in detail. A decision variable is an input whose value can be chosen by a decision maker. Decision variables enable you to harness the power of Crystal Ball and OptQuest to find optimal solutions. A first look at real options is included in this chapter.

Chapter 6 lists and explains the runtime options available in Crystal Ball as well as how and when to use them.

Chapter 7 discusses the relative merits of using the concepts of net present value and internal rate of return in deterministic and stochastic models. Examples include capital budgeting in finance and customer lifetime value in marketing.

Chapter 8 describes how to add stochastic assumptions to pro forma financial statements, then perform sensitivity analyses using tornado charts and Crystal Ball sensitivity charts.

Chapter 9 presents examples of using Crystal Ball to construct single and multiperiod portfolio models. It also compares the Crystal Ball results for a single-period model to the analytic solution in a special case where an analytic solution can be found.

Chapter 10 discusses Value at Risk (VaR) and its more sophisticated cousin, Conditional Value at Risk (CVaR), the relative merits of VaR and CVaR, and how they are used in risk management.

Chapter 11 describes how to simulate financial time series with Crystal Ball. It covers random walks, geometric Brownian motion, and mean-reverting models, as well as a discussion of autocorrelation and how to detect it in empirical data.

Chapter 12 shows how to create Crystal Ball models for financial option pricing, covering European, American, and exotic options. It includes a model to demonstrate how to simulate returns from option strategies, using a bull spread as an example. It also shows how to use Crystal Ball to evaluate a relatively new derivative security, a principal-protected instrument.

Chapter 13 is a discussion of how Crystal Ball and OptQuest are used to value real options. It also contains a brief review of the literature and some applications of real options analysis.

Chapter 14 presents a basic credit model that is widely used in banking. It also contains a short description of how this model may be useful for credit risk management.

Chapter 15 presents the use of Crystal Ball and OptQuest for optimal selection of different methods available to accomplish steps required to complete a construction project. However, the methodology is applicable to project management in virtually any other industry as well.

Chapter 16 discusses the use of Crystal Ball in oil and gas well exploration. It contains a simplified model of the use of simulation to help determine where to drill new wells to tap oil and gas reservoirs.

Appendix A contains short descriptions of each available Crystal Ball assumption. Each description includes the assumption’s parameters, probability mass or density function, cumulative distribution function, mean, standard deviation, and notes about the distribution and/or its usage.

Appendix B provides a brief description of how Crystal Ball generates the random numbers and variates during the simulation process.

Appendix C describes some variance reduction techniques, methods by which an analyst changes a model to get more precise estimates from a fixed number of trials during a simulation.

Appendix D provides information about downloading the Crystal Ball software and Excel files that are used in this book. A glossary of terms used in the book is included.

The references section in the text contains citations to academic and practitioner literature relating to financial modeling and risk analysis.

Acknowledgments

For their conversations and help (unwitting, by some) in writing this book I would like to thank: Stephanie Alger, Omar Alshihabi, Chris Anderson, Bill Beedles, Rishi Bhatnagar, George Bittlingmayer, David Blankinship, Eric Butz, Sarah Charnes, Barry Cobb, Tom Cowherd Jr., Riza Demirer, Amy Dougan, Bill Falloon, Dave Fredericks, Larry Goldman, Douglas Hague, Emilie Herman, Steve Hillmer, Joe B. Jones, David Kellogg, Paul Koch, Mike Krieger, Chad Lander, Charles Maner, Ivan Marcotte, Howard Marmorstein, Patrick McIntyre, Randy Miller, Girish Parakkal, John Pasinski, Samik Raychaudhuri, Catherine Shenoy, Prakash Shenoy, Steve Terbovich, Michael Tognetti, Lucie Trepanier, Eric Wainwright, Bruce Wallace, and John Walter. Special thanks go to Suzanne Swain Charnes for help with the art, and the time taken to indulge my interest in Crystal Ball over the years.

I enjoyed writing this book, and hope that it helps you learn how to build stochastic models of realistic situations important to you. I will appreciate any feedback that you care to send to [email protected].

JOHN CHARNES

Charlotte, North Carolina

2012

About the Author

Dr. John Charnes is currently President of the Risk Analytics and Predictive Intelligence Division (RAPID) of Syntelli Solutions, Inc. From 2007 to 2011, he was Senior Vice President in the Enterprise Credit Risk organization at Bank of America in Charlotte, North Carolina, USA.

He also served as professor and Scupin Faculty Fellow in the finance, economics, and decision sciences area at the University of Kansas School of Business, where he received both teaching and research awards, and was department chair from 2001 to 2004. Professor Charnes has taught courses in risk analysis, computer simulation, statistics, operations, quality management, and finance in the business schools of the University of Miami (Florida), University of Washington (Seattle), University of Minnesota (Minneapolis), and Hamline University (St. Paul).

He has published papers on financial risk analysis, statistics, and other topics in Financial Analysts Journal, The American Statistician, Management Science, Decision Sciences, Computers and Operation Research, Journal of the Operational Research Society, Journal of Business Logistics, and Proceedings of the Winter Simulation Conference. Dr. Charnes has engaged in research, consulting, and executive education for more than 100 corporations and other organizations in the United States and Canada.

John holds PhD, MBA, and bachelor of civil engineering degrees from the University of Minnesota. Before earning his doctorate, he worked as a surveyor, draftsman, field engineer, and quality-control engineer on numerous construction projects in Minnesota, Iowa, and Maryland. He has served as president of the Institute for Operations Research and the Management Sciences (INFORMS) College on Simulation, and proceedings coeditor (1996) and program chair (2002) for the Winter Simulation Conferences.

CHAPTER 1

Introduction

Life is stochastic. Anyone who works in business or finance today knows quite well that future events are highly unpredictable. We often proceed by planning for the worst outcome while hoping for the best, but most of us are painfully aware from experience that there are many risks and uncertainties associated with business endeavors. Even engineers who grew accustomed to calculating the precisely correct answer to textbook problems in school now realize that variation plays an important role in real-world problems.

Many analysts begin creating financial models of risky situations with a base case, constructed by making their best guess at the most likely value for each of the important inputs feeding a spreadsheet model, to calculate the output values that interest them. Often, they account for uncertainty by thinking of how each input in turn might deviate from the best guess and letting the spreadsheet calculate the consequences for the outputs. Such a “what-if” analysis provides insight into the sensitivity of the outputs to one-at-a-time changes in the inputs.