This fully updated and revised Second Edition of the Wilmott Award-winning book Inside Volatility Arbitrage demonstrates how to filter data using time series and financial econometrics to discover the best possible estimation of hidden opportunities given all the available information up to that point. All-new content includes estimation from historic option prices, instead of stocks, to gain better observation quality; spectral approaches and Wiener Chaos Expansions; and expanded in-depth examples of the statistical trading strategy.
In even greater detail, Javaheri shares in-depth information on the relationship between volatility and the stock and derivatives markets, detailed insights on Brownian motion for stock price returns, and option-pricing techniques such as inversion of the Fourier transform and mixing Monte Carlo.
Inside Volatility Filtering also illuminates how to:
- Effectively use a variety of models, from local volatility and stochastic volatility models to pure-jump models
- Accurately estimate model parameters using two possible sets of data-options prices and historic stock prices
- Best apply parametric inference methodologies to assets, and why you should question the consistency of information contained in the options and stock markets
Additional models and extra illustrative charts show you how to profit in these scenarios using the nuts and bolts of applied model calibration. Inside Volatility Filtering, Second Edition shows you a better way to approach abnormal distributions for more accurate volatility estimation.