Neuware - 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 sued to trade the markets mroe profitably. Filed with in-depth insight and expert advice, this book will focus on the idea of filtering.The idea behind filtering is to obtain the best possible estimation of a hidden state given all the available information up to that point. This estimation is done in an iterative manner in two stages: The first step is a time update in which the prior distribution from all the past information via a Chapman-Kolmogorov equation. The second step would then involve a measurement update where this prior distribution is used together with the conditional likelihood of the newest observation in order to compute the posterior distribution of the hidden state. The Bayes rule is used for this purpose. Once the posterior distribution is determined, it can be exploited for the optimal estimation of the hidden state.For practitioners and students, the author is adding content on:\* estimation from historic option prices instead of stocks, as the observation quality is better\* spectral approaches and in particular Wiener Chaos Expansions\* on the statistical trading strategy in section 3.

Bestandsnummer des Verk�ufers 9781118943977

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Bibliografische Details

Titel: Inside Volatility Filtering : Secrets of the Skew
Verlag: Wiley Aug 2015
Erscheinungsjahr: 2015
Sprache: Englisch
ISBN-10: 111894397X
ISBN-13: 9781118943977
Einband: Buch
Auflage: 2. Auflage
Zustand: Neu

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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.

�ber 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.

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