Inside Volatility Filtering: Secrets of the Skew

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9781118943977: Inside Volatility Filtering: Secrets of the Skew
Présentation de l'éditeur :

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.

Quatrième de couverture :

"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

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

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Description du livre John Wiley Sons Inc, United States, 2015. Hardback. État : New. 2nd Revised edition. 234 x 172 mm. Language: English . Brand New Book. 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. N° de réf. du libraire AAH9781118943977

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Description du livre John Wiley Sons Inc, United States, 2015. Hardback. État : New. 2nd Revised edition. 234 x 172 mm. Language: English . Brand New Book. 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. N° de réf. du libraire AAH9781118943977

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