Inside Volatility Arbitrage : The Secrets of Skewnessby Alireza JavaheriHardcover: 384 pages Publisher: John Wiley & Sons Inc (September 30, 2005) ISBN: 0471733873 FinMath.com

Inside Volatility Arbitrage: The Secrets of Skewness by Alireza Javaheri will also be available through the Wilmott Bookshop once it has been published.Laura

Thanks, this is actually my dissertation, which many of you have helped me with!in particular the section on inference from stochastic volatlity time-series

congrats! looks interesting. can we see a table of contents?

sure,here is a non-final version of the table of contents, sorry it is rather awkward but it is all in laTex ...------------------------------------------------------------------------------------------------Introduction Summary Contributions and Further Research Data and Programs 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------ Link to Credit Spread--- Level Dependent Volatility------ The Constant Elasticity Variance Approach------ The Bensoussan Crouhy Galai Approach Local Volatility--- The Dupire Approach------ The Breeden \& Litzenberger Identity------ The Dupire Identity------ Local Volatility vs Instantaneous Volatility--- 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------ Remark on the Gamma Distribution------ Stochastic Volatility vs Time-Changed processes--- Variance Gamma with Stochastic Arrival------ Option Pricing under VGSA------ The Characteristic Function--- Variance Gamma with Gamma Arrival RateThe Inference Problem Introduction Using Option Prices--- Direction Set (Powell) Method--- Numeric Tests--- The Distribution of the Errors Using Stock Prices--- The Likelihood Function------ The Justification for the MLE------ Likelihood Evaluation and Filtering--- Filtering------ Interpretation of the Kalman Gain--- The Simple and Extended Kalman Filters------ Another Interpretation of the Kalman Gain------ Residuals, MPE and RMSE--- The Unscented Kalman Filter--- Kushner's Non-Linear Filter------ Details of the Kushner algorithm--- Parameter Learning------ An Illustration------ Joint Filtering Examples------ Observability------ The One-Dimensional State within the Joint Filter------ Joint Filters and Time Interval--- Parameter Estimation via MLE------ An Illustration------ Stochastic Volatility Examples------ Optimization-Constraints for the Square-Root Model------ An Alternative Implementation------ The One-Dimensional State------ Other stochastic volatility models--- Diagnostics------ Chi-Square Test------ Box-Ljung Test------ Test Results------ Variogram--- Particle Filtering------ Underlying Theory------ Resampling------ Implementation------ An Illustration------ Application to the Heston Model------ Test Results------ Error Size------ The MH Enhancement--- Comparing Heston with other Models------ The Models------ The Results------ Parameter Learning Revisited--- The Performance of the Inference Tools------ Sample Size------ Joint Estimation of the Parameters------ Error Size revisited------ High Frequency Data------ The Frequency of the Observations------ Sampling Distribution--- The Bayesian Approach------ The Gibbs Sampler------ A Simple Illustration------ The Metropolis-Hastings Algorithm------ Illustration------ A Few Distributions------ Regression Analysis------ Application to Gaussian SV Models (Heston)--- Using the Characteristic Function--- Introducing Jumps------ The Model------ The Generic Particle Filter------ Extended/ Unscented Particle Filters------ The Srivastava Approach------ Numeric results------ The Optimization Algorithm--- Pure-Jump Models------ VG------ VGSA------ The Filtering Algorithm------ Parameter Estimation------ A More Efficient Algorithm------ An Extended/ Unscented Particle Filter------ Numeric Results------ Diagnostics------ VGG------ A Bayesian Approach for VGSA Recapitulation--- Model Identification--- Convergence Issues and SolutionsThe Consistency Problem Introduction The Consistency Test--- The Setting--- The Cross-Sectional Results------ Robustness Issues for the Cross-Sectional Method--- Time-Series Results------ Robustness Issues for the Time-Series Method--- Financial Interpretation The ``Peso'' Theory--- Background--- Numeric Results Trading Strategies--- Skewness Trades--- Kurtosis Trades--- Directional Risks------ Skewness vs Kurtosis--- An Exact Replication--- The Mirror Trades--- An Example of the Skewness Trade------ The Options Bid-Ask Spread------ Early Termination------ Implied Volatility Term-Structure------ Which Hedge-Ratio should we use?--- Multiple Trades--- High Volatility-of-Volatility and High Correlation Non-Gaussian Case--- VGSA------ VGSA vs VG------ Cross-Sectional vs Time-Series VGSA A Word of Caution Foreign Exchange, Fixed Income and Other Markets--- Foreign Exchange--- Fixed Income------ The Time-Series------ The Cross-Section Bibliography

looks good, but it appears we need to wait until sep!

Seems impressive, exhaustive, relevant and deep.

wow, coming from Nassim, we should put that on the back-cover !

Reza,Is it likely for you to put one of sample chapters here? I will be pleased to read it.

here is the introduction/ bibliography