Inside Volatility Arbitrage: The Secrets of Skewness(Wiley Finance)

Today's traders want to know when volatility is a sign that the sky is falling (and they should stay out of the market), and when it is a sign of a possible trading opportunity. "Inside Volatility Arbitrage" can help them do this. Author and financial expert Alireza Javaheri uses the classic approach to evaluating volatility - time series and financial econometrics - in a way that he believes is superior to methods presently used by market participants. He also suggests that there may be 'skewness' trading opportunities that can be used to trade the markets more profitably. Filled with in-depth insight and expert advice, "Inside Volatility Arbitrage" will help traders discover when 'skewness' may present valuable trading opportunities as well as why it can be so profitable.

Table of Contents:
Illustrations. Acknowledgments. Introduction. Summary. Contributions and Further Research. Data and Programs. Chapter 1: The Volatility Problem. Introduction. The Stock Market. The Stock Price Process. Historic Volatility. The Derivatives Market. The Black-Scholes Approach. The Cox-Ross-Rubinstein Approach. Jump Diffusion and Level-Dependent Volatility. Jump Diffusion. Level-Dependent Volatility. Local Volatility. The Dupire Approach. The Derman-Kani Approach. Stability Issues. Calibration Frequency. Stochastic Volatility. Stochastic Volatility Processes. GARCH and Diffusion Limits. The Pricing PDE Under Stochastic Volatility. The Market Price of Volatility Risk. The Two-Factor PDE. The Generalized Fourier Transform. The Transform Technique. Special Cases. The Mixing Solution. The Romano-Touzi Approach. A One-Factor Monte Carlo Technique. The Long-Term Asymptotic Case. The Deterministic Case. The Stochastic Case. A Series Expansion on Volatility-of-Volatility. Pure-Jump Models. Variance Gamma. Variance Gamma with Stochastic Arrival. Variance Gamma with Gamma Arrival Rate. Chapter 2: The Inference Problem. Introduction. Using Option Prices. Direction Set (Powell) Method. Numeric Tests. The Distribution of the Errors. Using Stock Prices. The Likelihood Function. Filtering. The Simple and Extended Kalman Filters. The Unscented Kalman Filter. Kushner's Nonlinear Filter. Parameter Learning. Parameter Estimation via MLE. Diagnostics. Particle Filtering. Comparing Heston with Other Models. The Performance of the Inference Tools. The Bayesian Approach. Using the Characteristic Function. Introducing Jumps. Pure Jump Models. Recapitulation. Model Identification. Convergence Issues and Solutions. Chapter 3: The Consistency Problem. Introduction. The Consistency Test. The Setting. The Cross-Sectional Results. Robustness Issues for the Cross-Sectional Method. Time-Series Results. Financial Interpretation. The Peso Theory. Background. Numeric Results. Trading Strategies. Skewness Trades. Kurtosis Trades. Directional Risks. An Exact Replication. The Mirror Trades. An Example of the Skewness Trade. Multiple Trades. High Volatility-of-Volatility and High Correlation. Non-Gaussian Case. VGSA. AWord of Caution. Foreign Exchange, Fixed Income, and Other Markets. Foreign Exchange. Fixed Income. References. Index.

About the Author :
ALIREZA JAVAHERI, PhD, CFA, is an adjunct researcher in the Finance and Economics Department of Ecole des Mines de Paris. He has worked in the financial industry for many years with companies such as Citigroup, Lehman Brothers, and Goldman Sachs. He has written numerous articles in various financial journals.

Review :
"...ideal for academics and practitioners who want to focus on volatility modeling. . . All of this makes the book rich and valuable. . . Go and get it!" (Wilmott magazine, September 2005) "Best New Quantitative Finance Book of the Year" (Wilmott Awards 2006)