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Inside Volatility Filtering

Author: Alireza Javaheri

Publisher: John Wiley & Sons

Published: 2015-07-27

Total Pages: 325

ISBN-13: 1118943988

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.

Inside Volatility Arbitrage

Author: Alireza Javaheri

Publisher: John Wiley & Sons

Published: 2011-08-24

Total Pages: 222

ISBN-13: 1118161025

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Today?s traders want to know when volatility is a sign that the sky is falling (and they should stay out of the market), and when it is a sign of a possible trading opportunity. Inside Volatility Arbitrage can help them do this. 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 used to trade the markets more profitably. Filled with in-depth insight and expert advice, Inside Volatility Arbitrage will help traders discover when "skewness" may present valuable trading opportunities as well as why it can be so profitable.

Inside Volatility Filtering

Author: Alireza Javaheri

Publisher: John Wiley & Sons

Published: 2015-08-24

Total Pages: 325

ISBN-13: 111894397X

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An Introduction to Wavelets and Other Filtering Methods in Finance and Economics

Author: Ramazan Gençay

Publisher: Elsevier

Published: 2001-10-12

Total Pages: 383

ISBN-13: 0080509223

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An Introduction to Wavelets and Other Filtering Methods in Finance and Economics presents a unified view of filtering techniques with a special focus on wavelet analysis in finance and economics. It emphasizes the methods and explanations of the theory that underlies them. It also concentrates on exactly what wavelet analysis (and filtering methods in general) can reveal about a time series. It offers testing issues which can be performed with wavelets in conjunction with the multi-resolution analysis. The descriptive focus of the book avoids proofs and provides easy access to a wide spectrum of parametric and nonparametric filtering methods. Examples and empirical applications will show readers the capabilities, advantages, and disadvantages of each method. The first book to present a unified view of filtering techniques Concentrates on exactly what wavelets analysis and filtering methods in general can reveal about a time series Provides easy access to a wide spectrum of parametric and non-parametric filtering methods

Recent Advances and Trends in Nonparametric Statistics

Author: M.G. Akritas

Publisher: Elsevier

Published: 2003-10-31

Total Pages: 522

ISBN-13: 0080540376

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The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection of short articles - most of which having a review component - describing the state-of-the art of Nonparametric Statistics at the beginning of a new millennium. Key features: • algorithic approaches • wavelets and nonlinear smoothers • graphical methods and data mining • biostatistics and bioinformatics • bagging and boosting • support vector machines • resampling methods

Stochastic Filtering with Applications in Finance

Author: Ramaprasad Bhar

Publisher: World Scientific

Published: 2010

Total Pages: 354

ISBN-13: 9814304859

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This book provides a comprehensive account of stochastic filtering as a modeling tool in finance and economics. It aims to present this very important tool with a view to making it more popular among researchers in the disciplines of finance and economics. It is not intended to give a complete mathematical treatment of different stochastic filtering approaches, but rather to describe them in simple terms and illustrate their application with real historical data for problems normally encountered in these disciplines. Beyond laying out the steps to be implemented, the steps are demonstrated in the context of different market segments. Although no prior knowledge in this area is required, the reader is expected to have knowledge of probability theory as well as a general mathematical aptitude. Its simple presentation of complex algorithms required to solve modeling problems in increasingly sophisticated financial markets makes this book particularly valuable as a reference for graduate students and researchers interested in the field. Furthermore, it analyses the model estimation results in the context of the market and contrasts these with contemporary research publications. It is also suitable for use as a text for graduate level courses on stochastic modeling.

State-Space Models

Author: Yong Zeng

Publisher: Springer Science & Business Media

Published: 2013-08-15

Total Pages: 358

ISBN-13: 1461477891

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State-space models as an important mathematical tool has been widely used in many different fields. This edited collection explores recent theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series models, regime-switching and hidden Markov models, continuous- or discrete-time state processes, and models of equally-spaced or irregularly-spaced (discrete or continuous) observations. The contributed chapters are divided into four parts. The first part is on Particle Filtering and Parameter Learning in Nonlinear State-Space Models. The second part focuses on the application of Linear State-Space Models in Macroeconomics and Finance. The third part deals with Hidden Markov Models, Regime Switching and Mathematical Finance and the fourth part is on Nonlinear State-Space Models for High Frequency Financial Data. The book will appeal to graduate students and researchers studying state-space modeling in economics, statistics, and mathematics, as well as to finance professionals.

'Filtering Noise from Volatility'

Author: Alexander Izmailov

Publisher:

Published: 2014

Total Pages: 7

ISBN-13:

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Alexander Izmailov, Ph.D (theoretical physics) and Brian Shay, Ph.D (mathematics), of Market Memory Trading, L.L.C., present in a series of nine (9) white papers, aspects of a revolutionary advance in uncovering hidden dependencies via filtering noise from correlation matrices developed by the New York based company, Market Memory Trading, L.L.C. (MMT). Correlations are quantitative measures of these dependencies and noise filtering increases their accuracy as a decision-making tool, from asset allocation to LIBOR Surveillance and cyber security.“FILTERING NOISE FROM VOLATILITY.” White Paper 5, dated March 26, 2013, provides a demonstration of the omnipresence of noise in volatilities of returns of financial instruments; and a demonstration that more than 30% of SP500 securities can have percentage change in volatility of more than 10% as a result of noise filtering. Refer to Appendix A for Complete Series.

Alpha Trading

Author: Perry J. Kaufman

Publisher: John Wiley & Sons

Published: 2011-03-08

Total Pages: 325

ISBN-13: 0470529741

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From a leading trading systems developer, how to make profitable trades when there are no obvious trends How does a trader find alpha when markets make no sense, when price shocks cause diversification to fail, and when it seems impossible to hedge? What strategies should traders, long conditioned to trend trading, deploy? In Alpha Trading: Profitable Strategies That Remove Directional Risk, author Perry Kaufman presents strategies and systems for profitably trading in directionless markets and in those experiencing constant price shocks. The book Details how to exploit new highs and lows Describes how to hedge primary risk components, find robustness, and craft a diversification program Other titles by Kaufman: New Trading Systems and Methods, 4th Edition and A Short Course in Technical Trading, both by Wiley Given Kaufman's 30 years of experience trading in almost every kind of market, his Alpha Trading will be a welcome addition to the trading literature of professional and serious individual traders for years to come.

Kalman Filtering and Neural Networks

Author: Simon Haykin

Publisher: John Wiley & Sons

Published: 2004-03-24

Total Pages: 302

ISBN-13: 047146421X

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State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover: An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF) Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm The unscented Kalman filter Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.