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TABLE OF CONTENTS Introduction Thinking Out of the Box ? Improving Option Pricing Strategies: A Scientific Investigation ? Assumptions Made By Popular Models: Are They Correct? ? Optimal Model Inputs ? What's Covered in the Chapters? ? Who Will Benefit? ? Tools and Materials Used in the Investigation ? An Invitation Chapter 1 A Review of Option Basics Basic Options: Calls and Puts ? Covered and Naked ? Additional Option Terminology ? Factors Influencing Option Premium (well-known factors such as volatility, time, strike, stock price and interest rate; lesser-known factors such as skew, kurtosis, and cycles) ? Uses of Options ? Option Pricing Models (the Greeks, Black-Scholes, why use a pricing model?) ? Graphic Illustrations (the influence of various factors on option premium) ? Put-Call Parity, Conversions, and Reversals ? Synthetics and Equivalent Positions ? Summary ? Suggested Reading Chapter 2 Fair Value and Efficient Price Defining Fair Value ? Fair Value and the Efficient Market ? The Context Dependence of Fair Value ? Understanding and Estimating Fair Value ? Fair Value and Arbitrage ? Fair Value and Speculation ? Estimating Speculative Fair Value (modeling the underlying stock, pricing the option) ? Summary ? Suggested Reading Chapter 3 Popular Option Pricing Models The Cox-Ross-Rubinstein Binomial Model (specifying growth and volatility, Monte Carlo pricing, pricing with binomial trees) ? The Black-Scholes Model (the Black-Scholes formula, Black-Scholes and forward expectation, Black-Scholes versus binomial pricing) ? Means, Medians, and Stock Returns (empirical study of returns) ? Summary ? Suggested Reading Chapter 4 Statistical Moments of Stock Returns The First Four Moments (calculating sample moments, statistical features of sample moments) ? Empirical Studies of Moments of Returns (raw data, analytic software, Monte Carlo baselines) ? Study 1: Moments and Holding Period (results from segmented analysis: statistical independence of returns, log-normality of returns, estimating standard errors; results from non-segmented analysis: volatility and independence of returns, skew, kurtosis, and log-normality; non-segmented analysis of two indices) ? Study 2: Moments and Day of Week ? Study 3: Moments and Seasonality ? Study 4: Moments and Expiration ? Summary ? Suggested Reading Chapter 5 Estimating Future Volatility Measurement Reliability ? Model Complexity and Other Issues ? Empirical Studies of Volatility (software and data, calculation of implied volatility) ? Study 1: Univariate Historical Volatility as Predictor of Future Volatility (regression to the mean, quadratic/nonlinear relationship, changing relationship with changing volatility, straddle-based versus standard future volatility, longer-term historical volatility, raw data regressions) ? Study 2: Bivariate Historical Volatility of Future Volatility (independent contributions, reversion to long-term mean) ? Study 3: Reliability and Stability of Volatility Measures ? Study 4: Multivariate Prediction of Volatility (using two measures of historical volatility and three seasonal harmonics) ? Study 5: Implied Volatility ? Study 6: Historical and Implied Volatility Combined as a Predictor of Future Volatility (regression results, correlational analysis, path analysis) ? Study 7: Reliability of Implied Volatility ? Summary ? Suggested Reading Chapter 6 Pricing Options with Conditional Distributions Degrees of Freedom (problem of excessive consumption, curve-fitting, use of rescaling to conserve degrees of freedom) ? General Methodology ? Study 1: Pricing Options Using Conditional Distributions with Raw Historical Volatility ? Study 2: Pricing Options Using Conditional Distributions with Regression-Estimated Volatility (analytic method, deviant call premiums, other deviant premiums, non-deviant premiums) ? Study 3: Re-Analysis with Detrended Distributions ? Study 4: Skew and Kurtosis as Additional Variables When Pricing Options with Conditional Distributions (effect on out-of-the-money calls, out-of-the-money puts, in-the-money options, at-the-money options) ? Study 5: Effect of Trading Venue on Option Worth (out-of-the-money options, detrended distributions; at-the-money options, detrended distributions; out-of-the-money options, no detrending) ? Study 6: Stochastic Crossover and Option Value (out-of-the-money, detrended distributions; out-of-the-money, raw distributions; at-the-money options) ? Summary ? Suggested Reading Chapter 7 Neural Networks, Polynomial Regressions, and Hybrid Pricing Models Continuous Nonlinear Functions ? Construction of a Pricing Function ? Polynomial Regression Models ? Neural Network Models ? Hybrid Models ? General Overview ? Data ? Software ? Study 1: Neural Networks and Black-Scholes (can a neural network emulate Black-Scholes? test of a small neural network, test of a larger neural network) ? Study 2: Polynomial Regressions and Black-Scholes ? Study 3: Polynomial Regressions on Real-Market Data ? Study 4: Basic Neural Pricing Models ? Study 5: Pricing Options with a Hybrid Model ? Summary ? Suggested Reading Chapter 8 Volatility Revisited Data and Software ? Study 1: Volatility and Historical Kurtosis ? Study 2: Volatility and Historical Skew ? Study 3: Stochastic Oscillator and Volatility ? Study 4: Moving Average Deviation and Volatility ? Study 5: Volatility and Moving Average Slope ? Study 6: Range Percent and Volatility ? Study 7: Month and Volatility ? Study 8: Real Options and Volatility ? Summary ? Suggested Reading Chapter 9 Option Prices in the Marketplace Data and Software ? Method ? Results (calls on stocks with 30 percent historical volatility and with 90 percent historical volatility, puts on stocks with 30 percent historical volatility and with 90 percent historical volatility) ? Conclusion (discussion of issues, suggestions for further study) Conclusion Defining Fair Value ? Popular Models and their Assumptions (the assumptions themselves, strengths and weaknesses of popular models) ? Volatility Payoffs and Distributions ? Mathematical Moments (moments and holding periods, moments and distributions, moments and day of the week, moments and seasonality, moments and expiration date) ? Volatility (standard historical volatility as an estimator of future volatility, the reliability of different measures of volatility, developing a better estimator of future volatility, implied volatility) ? Conditional Distributions (historical volatility: conditional distributions vs. Black-Scholes; regression-estimated volatility: conditional distributions vs. Black-Scholes; detrended distributions: conditional distributions vs. Black-Scholes; distributions and the volatility payoff; skew and kurtosis as variables in a conditional distribution; conditional distributions and venue; technical indicators as conditioning variables) ? Using Nonlinear Modeling Techniques to Price Options (neural networks and polynomial regressions vs. Black-Scholes, strengths and weaknesses of nonlinear modeling techniques, hybrid models) ? Volatility Revisited (the impact of historical skew, kurtosis, and historical volatility on future volatility; using technical indicators in the prediction of future volatility) ? Option Prices in the Marketplace ? Conclusion Notice Companion Software Available Appendix References and Suggested Reading Index
Library of Congress Subject Headings for this publication:
Options (Finance) -- Prices -- Mathematical models.