You are previewing Financial Simulation Modeling in Excel.
O'Reilly logo
Financial Simulation Modeling in Excel

Book Description

"I've worked with simulation in business for over 20 years, and Allman really nails it with this book. I admit that I own his previous book on structured finance cash flows, but I was surprised by what I found in here. He addresses the fundamental questions of how decision makers react to simulations and his read was very much in accordance with what I've experienced myself. When it came to the nuts and bolts of describing the different types of simulation analysis the book becomes incredibly detailed. There is working code and models for a fantastic array of the most common simulation problems. If you're so inclined, the book very carefully steps through the tricky math needed to really understand the theory behind stochastic modeling in finance. If you're preparing models that include any kind of randomization or stochastic modeling component, this book is a must-read, a tremendous value and time-saver." — David Brode of The Brode Group

A practical guide to understanding and implementing financial simulation modeling

As simulation techniques become more popular among the financial community and a variety of sub-industries, a thorough understanding of theory and implementation is critical for practitioners involved in portfolio management, risk management, pricing, and capital budgeting. Financial Simulation Modeling in Excel contains the information you need to make the most informed decisions possible in your professional endeavors.

Financial Simulation Modeling in Excel contains a practical, hands-on approach to learning complex financial simulation methodologies using Excel and VBA as a medium. Crafted in an easy to understand format, this book is suitable for anyone with a basic understanding of finance and Excel. Filled with in-depth insights and expert advice, each chapter takes you through the theory behind a simulation topic and the implementation of that same topic in Excel/VBA in a step-by-step manner.

  • Organized in an easy-to-follow fashion, this guide effectively walks you through the process of creating and implementing risk models in Excel

  • A companion website contains all the Excel models risk experts and quantitative analysts need to practice and confirm their results as they progress

  • Keith Allman is the author of other successful modeling books, including Corporate Valuation Modeling and Modeling Structured Finance Cash Flows with Microsoft Excel

Created for those with some background in finance and experience in Excel, this reliable resource shows you how to effectively perform sound financial simulation modeling, even if you've yet to do extensive modeling up to this point in your professional or academic career.

"The ebook version does not provide access to the companion files".

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Contents
  5. Preface
  6. Acknowledgments
  7. About the Authors
  8. CHAPTER 1: Introduction
    1. WHAT IS SIMULATION?
    2. CHARACTERISTICS OF A SIMULATION
    3. INSTRUCTIONAL METHODOLOGY
    4. HOW THIS BOOK WORKS
    5. ABOUT THE COMPANION WEBSITE
    6. EXCEL 2003 AND EARLIER VERSUS EXCEL 2007/2010
    7. A FEW WORDS ABOUT SEMANTICS
    8. FINAL REMINDERS
  9. CHAPTER 2: Random Numbers, Distributions, and Basic Simulation Setup
    1. SEED VARIABLES AND RANDOM NUMBER GENERATION
    2. DISTRIBUTIONS
    3. NORMAL PSEUDORANDOM NUMBERS
    4. QUICKLY GENERATING NORMAL PSEUDORANDOM NUMBERS USING PREBUILT EXCEL FUNCTIONS
    5. OTHER METHODS OF GENERATING NORMAL PSEUDORANDOM NUMBERS
    6. PUTTING TOGETHER A MORE DEVELOPED SIMULATION USING THE FUNDAMENTAL COMPONENTS
    7. BROWNIAN MOTION AND WIENER PROCESSES
    8. UNDERSTANDING SIMULATION RESULTS
    9. THIS IS JUST THE BEGINNING
  10. CHAPTER 3: Correlation
    1. THE BASIC IDEA OF CORRELATION
    2. CORRELATION IN A FINANCIAL CONTEXT
    3. PRODUCING SETS OF CORRELATED NORMAL RANDOM NUMBERS USING MATRIX MATHEMATICS
    4. GOING FORWARD USING OUR TOOLS
  11. CHAPTER 4: Option Pricing
    1. BINOMIAL TREES
    2. HULL-WHITE INTEREST RATE MODEL
    3. HULL-WHITE TRINOMIAL TREE
    4. TERM STRUCTURE RECOVERY USING FORWARD INDUCTION
    5. BLACK-SCHOLES OPTION PRICING METHOD
    6. DISCUSSION ON ERRORS
    7. BEYOND PRICE MOVEMENTS
  12. CHAPTER 5: Corporate Default Simulation
    1. THE THEORY BEHIND THE MERTON MODEL
    2. SHORT-TERM AND LONG-TERM LIABILITIES: THE BARRIER AND CALIBRATION
    3. TUNING THE MODEL—DATA SETS AND DRIFT
    4. OTHER METHODS TO DEVELOP DEFAULT PROBABILITIES
    5. DETERMINING LOSS PROBABILITIES FROM BOND PRICES OR CREDIT DEFAULT SWAPS
    6. BOND TERM STRUCTURE AND BOOTSTRAPPING
    7. RECOVERY ASSUMPTIONS
    8. SOVEREIGNS AND MUNICIPALS: NONCORPORATE ISSUERS
    9. FROM ASSETS TO POOLS
  13. CHAPTER 6: Simulating Pools of Assets
    1. DIFFERENCES BETWEEN PROJECTING DEFAULTS AND PRICE MOVEMENTS
    2. VALUE AT RISK (VAR)
    3. STRUCTURED PRODUCTS AND CDOS
    4. TRANSITION MATRICES
    5. KNOWING YOUR ASSETS
  14. CHAPTER 7: Dealing with Data Deficiencies and Other Issues
    1. “GARBAGE IN, GARBAGE OUT”
    2. INCONSISTENT DATA FORMATS
    3. LACK OF DATA
  15. CHAPTER 8: Advanced Topics and Further Reading
    1. VBA AND OTHER LANGUAGES: DIFFERENT PROGRAMS FOR DIFFERENT USES
    2. QUASI–MONTE CARLO AND COMPUTATIONAL TIME
    3. EFFICIENT MARKET HYPOTHESIS AND ITS WEAKNESSES
    4. DISTRIBUTIONS: NASSIM TALEB AND NORMALCY
    5. NEW FRONTIERS OF ECONOPHYSICS
    6. WORKING WITH IMPERFECT MODELS
  16. APPENDIX A: Partial Differential Equations
    1. PARTIAL DERIVATIVES
    2. ORDINARY DIFFERENTIAL EQUATIONS (ODE)
    3. BOUNDARY CONDITIONS
    4. PARTIAL DIFFERENTIAL EQUATIONS
    5. STOCHASTIC DIFFERENTIAL EQUATIONS
  17. APPENDIX B: Newton-Raphson Method
  18. References
    1. FURTHER READING
  19. Index