Inside Volatility Filtering: Secrets of the Skew (Wiley Finance) - Hardcover

Javaheri, Alireza

Hardcover

ISBN 10: 111894397X ISBN 13: 9781118943977

Verlag: Wiley, 2015

Inhaltsangabe

A new, more accurate take on the classical approach to volatility evaluation

Inside Volatility Filtering presents a new approach to volatility estimation, using financial econometrics based on a more accurate estimation of the hidden state. Based on the idea of "filtering", this book lays out a two-step framework involving a Chapman-Kolmogorov prior distribution followed by Bayesian posterior distribution to develop a robust estimation based on all available information. This new second edition includes guidance toward basing estimations on historic option prices instead of stocks, as well as Wiener Chaos Expansions and other spectral approaches. The author's statistical trading strategy has been expanded with more in-depth discussion, and the companion website offers new topical insight, additional models, and extra charts that delve into the profitability of applied model calibration. You'll find a more precise approach to the classical time series and financial econometrics evaluation, with expert advice on turning data into profit.

Financial markets do not always behave according to a normal bell curve. Skewness creates uncertainty and surprises, and tarnishes trading performance, but it's not going away. This book shows traders how to work with skewness: how to predict it, estimate its impact, and determine whether the data is presenting a warning to stay away or an opportunity for profit.

• Base volatility estimations on more accurate data
• Integrate past observation with Bayesian probability
• Exploit posterior distribution of the hidden state for optimal estimation
• Boost trade profitability by utilizing "skewness" opportunities

Wall Street is constantly searching for volatility assessment methods that will make their models more accurate, but precise handling of skewness is the key to true accuracy. Inside Volatility Filtering shows you a better way to approach non-normal distributions for more accurate volatility estimation.

Über diesen Titel

Über die Autorin bzw. den Autor

ALIREZA JAVAHERI is the head of Equities Quantitative Research Americas at JP Morgan and an adjunct professor of Mathematical Finance at the Courant Institute of New York University, as well as Baruch College. He has worked in the field of derivatives quantitative research since 1994 in a variety of investment banks, including Goldman Sachs and Citigroup.

Von der hinteren Coverseite

"While e-trading typically starts with cash instruments and vanilla securities, it is inevitable that it will eventually encompass trading activities that lean heavily on quantitative elements such as volatility trading. As a result, the Second Edition of this book serves its intended audience well, providing an up-to-date, comprehensive review of the application of filtering techniques to volatility forecasting. While the title of each chapter is framed as a problem, the contents of each chapter represent our best guess at the answer. Employing the advances that econometricians have made in the past quarter century, the fraction of variance explained is a truly impressive accomplishment."
—From the Foreword by Peter Carr, Global Head of Market Modeling, Morgan Stanley; and Executive Director, Masters in Math Finance Program, Courant Institute, New York University

The New, More Accurate Take on the Classical Approach to Volatility Evaluation

Inside Volatility Filtering, Second Edition presents a new approach to volatility estimation identifying financial econometrics based on a more accurate estimation of the hidden state. Based on the idea of "filtering," this practical guide lays out a two-step framework involving a Chapman-Kolmogorov prior distribution followed by Bayesian posterior distribution to develop a robust estimation based on all available information. This new edition gives you an edge by showing you how to:

• Base volatility estimations on more accurate data
• Integrate past observation with Bayesian probability
• Exploit posterior distribution of the hidden state for optimal estimation
• Boost trade profitability by identifying "skewness" opportunities

Aus dem Klappentext

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.

Details zur Ausgabe

• Verlag: Wiley
• Erscheinungsdatum: 2015
• Sprache: Englisch
• ISBN 10: 111894397X
• ISBN 13: 9781118943977
• Einband: Tapa dura
• Auflage: 2
• Anzahl der Seiten: 320
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: Nicht verfügbar