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Book Description

This book makes quantitative finance (almost) easy! Its new visual approach makes quantitative finance accessible to a broad audience, including those without strong backgrounds in math or finance. Michael Lovelady introduces a simplified but powerful technique for calculating profit probabilities and graphically representing the outcomes. Lovelady's "pictures" highlight key characteristics of structured securities such as the increased likelihood of profits, the level of virtual dividends being generated, and market risk exposures. After explaining his visual approach, he applies it to one of today's hottest investing trends: lower-volatility, higher-income strategies. Because of today's intense interest in alternative investments and structured securities, this book reviews their unique advantages to investors, managers and advisors of retail and institutional portfolios. Visual Quantitative Finance focuses on key topics directly related to the design, pricing and communication of structured securities, including stochastic price projections and the framework underlying options pricing formulas. The key is Lovelady's explicit use of probabilities in a spreadsheet format. By working directly with the underlying assumptions, he transforms the Black-Scholes framework into five columns of a simple Excel spreadsheet, with no complicated formulas—making structured securities far more intuitive to design, evaluate and manage.

For all investors, students, and financial professionals who are interested in quantitative finance, risk measurement, options pricing, structured securities, or financial model building - and for everyone who needs to explain these topics to someone else. For those with quantitative backgrounds, this guide offers powerful new tools for design and risk management, simplifying the design and evaluation of innovative instruments. For everyone else, Lovelady makes the subject comprehensible for the first time.

Table of Contents

  1. Title Page
  2. Copyright Page
  3. Contents
  4. Acknowledgments
  5. About the Author
  6. Preface
  7. 1. Introduction
    1. Growth in Structured Securities
    2. Growing Emphasis on Low Volatility and Dividends
    3. Criticisms of Structured Securities
    4. Demand for Quantitative Skills
    5. Direction of Quantitative Finance
    6. When I Realized It Might Be Easier
    7. Try Again
    8. The Spreadsheet
    9. Visualizing the Result
    10. What It Means and Why It Works: A Nontechnical Overview
    11. It Doesn’t Get Too Complicated
    12. An Integrated View of Risk Management
    13. Endnotes
  8. 2. Random Variables and Option Pricing
    1. Random Variables
    2. Building the Spreadsheet
    3. Correcting the Mistake
    4. Optional: Additional Resources
  9. 3. An Overview of Option Pricing Methods
    1. The Black-Scholes Formula
    2. Black-Scholes Assumptions
    3. The Binomial Option Pricing Method
    4. Monte Carlo Methods
    5. Putting Visual Quant in Context
    6. Additional Reading, Advanced Topics, and Resources
    7. Endnotes
  10. 4. Value-at-Risk and Conditional Value-at-Risk
    1. How Likely Is Something?
    2. Value-at-Risk
    3. Multiple Stock VaR
    4. Stock and Option VaR
    5. Conditional Value-at-Risk
  11. 5. Full Black-Scholes Model
    1. Adding Functionality to the Model
    2. Stock Return Mean (Cell G3)
    3. Stock Return Standard Deviation (Cell G4)
    4. Discount Factor
    5. Stock Price Median
    6. Summary of New Formulas
    7. Pricing Put Options
    8. Effects of Assumption Changes
    9. Endnote
  12. 6. The Lognormal Distribution and Calc Engine
    1. Definition of the Lognormal Distribution
    2. The Forward Equation
    3. Cross Reference: Stochastic Differential Equations
    4. The Backward Equation
    5. The Calc Engine
    6. Assigning Probabilities
    7. Setting the Stock Price Range
    8. Visualizing Option Pricing As Normal or Lognormal
  13. 7. Investment Profiles and Synthetic Annuities
    1. What Is a Synthetic Annuity, and How Does It Work?
    2. The Investment Profile
    3. Assigning Probabilities Using Implied Volatility
    4. Using Options to Reshape the Investment Profile
    5. Adjusting the Profile for Behavioral Finance
    6. Concentrated Stock Example
    7. The Synthetic Annuity in Turbulent Markets
  14. 8. Stock-Only Investment Profile
    1. The Purpose and Context of the Model
    2. The Stock-Only Investment Profile
    3. The Calc Engine
    4. The Stock-Only Profit Calculation
    5. Adding the Chart
    6. Test: Stock-Only Investment Profile
  15. 9. Adding Options to the Model
    1. Long Put Profit
    2. Short Put
    3. Expected Values
    4. Black-Scholes Add-In
    5. The Heading Formulas
    6. Delta Formulas
    7. Time Value and Total Premium Formulas
  16. 10. Option Investment Profiles
    1. Long Call Option Investment Profile
    2. Short Call Option
    3. Long Put Option
    4. Short Put Option
  17. 11. Covered Calls, Condors, and SynAs
    1. Covered Call Investment Profile
    2. Put–Call Parity
    3. Iron Condor Investment Profile
    4. Synthetic Annuity (SynA) Investment Profile
    5. Adding a Customized Utility Function
    6. Endnotes
  18. 12. Understanding Price Changes
    1. Investing in XYZ
    2. Attribution: Explaining Why the Option Price Changed
    3. Endnote
  19. 13. The Greeks
    1. The Option Greeks
    2. Calculating Greeks: Formulas, Models, and Platforms
    3. Delta
    4. Theta
    5. Vega
  20. 14. Tracking Performance
    1. Tracking Template
    2. TradeStation Platform
    3. Putting It All Together: Synthetic Annuity Overview
  21. Introduction to Chapters 14, “Tracking Performance,” and 15, “Covered Synthetic Annuities”
  22. 15. Covered Synthetic Annuities
    1. Covered Synthetic Annuity (CSynA)
    2. Example: Deere & Company
    3. The Standard CSynA
    4. Supplemental Material: The CBOE S&P 500 BuyWrite Index
    5. BXM Study by Callan Associates
  23. Index