Handbook of Volatility Models and Their Applications
Handbook of Volatility Models and Their Applications

Author: Luc Bauwens

Publisher: John Wiley & Sons

Published: 2012-04-17

Total Pages: 566

ISBN-13: 0470872519

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A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.

High-Frequency Financial Econometrics
High-Frequency Financial Econometrics

Author: Yacine Aït-Sahalia

Publisher: Princeton University Press

Published: 2014-07-21

Total Pages: 683

ISBN-13: 0691161437

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A comprehensive introduction to the statistical and econometric methods for analyzing high-frequency financial data High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. Yacine Aït-Sahalia and Jean Jacod cover the mathematical foundations of stochastic processes, describe the primary characteristics of high-frequency financial data, and present the asymptotic concepts that their analysis relies on. Aït-Sahalia and Jacod also deal with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As they demonstrate, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. Aït-Sahalia and Jacod approach high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.

Volatility
Volatility

Author: Robert A. Schwartz

Publisher: Springer Science & Business Media

Published: 2010-11-18

Total Pages: 152

ISBN-13: 1441914749

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Volatility is very much with us in today's equity markets. Day-to-day price swings are often large and intra-day volatility elevated, especially at market openings and closings. What explains this? What does this say about the quality of our markets? Can short-period volatility be controlled by better market design and a more effective use of electronic technology? Featuring insights from an international array of prominent academics, financial markets experts, policymakers and journalists, the book addresses these and other questions concerning this timely topic. In so doing, we seek deeper knowledge of the dynamic process of price formation, and of the market structure and regulatory environment within which our markets function. The Zicklin School of Business Financial Markets Series presents the insights emerging from a sequence of conferences hosted by the Zicklin School at Baruch College for industry professionals, regulators, and scholars. Much more than historical documents, the transcripts from the conferences are edited for clarity, perspective and context; material and comments from subsequent interviews with the panelists and speakers are integrated for a complete thematic presentation. Each book is focused on a well delineated topic, but all deliver broader insights into the quality and efficiency of the U.S. equity markets and the dynamic forces changing them.

Volatility Components, Leverage Effects, and the Return-Volatility Relations
Volatility Components, Leverage Effects, and the Return-Volatility Relations

Author: Junye Li

Publisher:

Published: 2010

Total Pages: 0

ISBN-13:

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This paper investigates the return-volatility relation by taking into account the model specification problem. The market volatility is modeled to have two components, one due to the diffusion risk and the other due to the jump risk. The model indicates that under the absence of leverage effects, it becomes a variant of Merton's ICAPM, while under the existence of leverage effects, the return-volatility relations are determined by interactions between risk premia and leverage effects. Empirically, I find a robust negative relationship between the excess return and the jump volatility, whereas the relationship between the excess return and the diffusion volatility is hard to identify notwithstanding that the indirect evidence of the positive relationship exists.

Volatility-Based Technical Analysis
Volatility-Based Technical Analysis

Author: Kirk Northington

Publisher: John Wiley & Sons

Published: 2009-08-11

Total Pages: 480

ISBN-13: 0470522305

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A framework for creating volatility-based technical analysis and trading it for profit Volatility-Based Technical Analysis bridges the advantage gap between resource rich institutions and individual traders. It is a no-calculus, plain-English text that reveals original, highly technical, mathematical-based volatility indicators, complete with MetaStock® and TradeStation® code. With this in hand, any trader can "trade the invisible" by seeing a hidden mathematical structure on the price chart. Author Kirk Northington reveals his proprietary volatility indicators that serve as a market early warning system. Northington extensively teaches you how to build your own indicators, test them, and incorporate your original components into your specific trading methods. Walks traders through the mathematical techniques needed to create indicators that fit their own style Illustrates volatility-based entries and exits with over 170 descriptive chart examples Introduces two new concepts in technical analysis: Volatility Shift and PIV Written with the serious trader in mind, Volatility-Based Technical Analysis has what you need to successfully trade today's institutionally dominated markets.

Multifractal Volatility
Multifractal Volatility

Author: Laurent E. Calvet

Publisher: Academic Press

Published: 2008-10-13

Total Pages: 273

ISBN-13: 0080559964

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Calvet and Fisher present a powerful, new technique for volatility forecasting that draws on insights from the use of multifractals in the natural sciences and mathematics and provides a unified treatment of the use of multifractal techniques in finance. A large existing literature (e.g., Engle, 1982; Rossi, 1995) models volatility as an average of past shocks, possibly with a noise component. This approach often has difficulty capturing sharp discontinuities and large changes in financial volatility. Their research has shown the advantages of modelling volatility as subject to abrupt regime changes of heterogeneous durations. Using the intuition that some economic phenomena are long-lasting while others are more transient, they permit regimes to have varying degrees of persistence. By drawing on insights from the use of multifractals in the natural sciences and mathematics, they show how to construct high-dimensional regime-switching models that are easy to estimate, and substantially outperform some of the best traditional forecasting models such as GARCH. The goal of Multifractal Volatility is to popularize the approach by presenting these exciting new developments to a wider audience. They emphasize both theoretical and empirical applications, beginning with a style that is easily accessible and intuitive in early chapters, and extending to the most rigorous continuous-time and equilibrium pricing formulations in final chapters. Presents a powerful new technique for forecasting volatility Leads the reader intuitively from existing volatility techniques to the frontier of research in this field by top scholars at major universities The first comprehensive book on multifractal techniques in finance, a cutting-edge field of research

Volatility Components
Volatility Components

Author: Zhongjin Lu

Publisher:

Published: 2008

Total Pages: 33

ISBN-13:

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In this paper we analyze CBOE VIX futures price time series data from Mar. 2004 to Feb. 2008. We derive a new pricing framework for VIX futures that is convenient to study variance term structure dynamics. Our main contribution to existing literature is the identification of the number of factors implicit in VIX futures term structure. We find that three-factor model is ideal to characterize the variance term structure. We further construct and estimate structured two- and three-factor models to identify the components and find similar results.

Volatility Trading, + website
Volatility Trading, + website

Author: Euan Sinclair

Publisher: John Wiley & Sons

Published: 2008-06-23

Total Pages: 228

ISBN-13: 0470181990

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In Volatility Trading, Sinclair offers you a quantitative model for measuring volatility in order to gain an edge in your everyday option trading endeavors. With an accessible, straightforward approach. He guides traders through the basics of option pricing, volatility measurement, hedging, money management, and trade evaluation. In addition, Sinclair explains the often-overlooked psychological aspects of trading, revealing both how behavioral psychology can create market conditions traders can take advantage of-and how it can lead them astray. Psychological biases, he asserts, are probably the drivers behind most sources of edge available to a volatility trader. Your goal, Sinclair explains, must be clearly defined and easily expressed-if you cannot explain it in one sentence, you probably aren't completely clear about what it is. The same applies to your statistical edge. If you do not know exactly what your edge is, you shouldn't trade. He shows how, in addition to the numerical evaluation of a potential trade, you should be able to identify and evaluate the reason why implied volatility is priced where it is, that is, why an edge exists. This means it is also necessary to be on top of recent news stories, sector trends, and behavioral psychology. Finally, Sinclair underscores why trades need to be sized correctly, which means that each trade is evaluated according to its projected return and risk in the overall context of your goals. As the author concludes, while we also need to pay attention to seemingly mundane things like having good execution software, a comfortable office, and getting enough sleep, it is knowledge that is the ultimate source of edge. So, all else being equal, the trader with the greater knowledge will be the more successful. This book, and its companion CD-ROM, will provide that knowledge. The CD-ROM includes spreadsheets designed to help you forecast volatility and evaluate trades together with simulation engines.

Volatility Components and Long Memory-Effects Revisited
Volatility Components and Long Memory-Effects Revisited

Author: Markus Haas

Publisher:

Published: 2007

Total Pages:

ISBN-13:

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The goal of this paper is to illuminate the capability of the component GARCH model of Ding and Granger (1996) and Engle and Lee (1999) to reproduce the long memory-type behavior of financial volatility. The potential of this model to capture the long memory dynamics observed in measures of financial volatility has been documented recently by Maheu (2005) and Deo et al. (2006), who base their conclusions on simulation techniques and a forecasting exercise, respectively. In this paper, a simple explanation for these observations is provided, which is based on the theoretical autocorrelation function (ACF) of the component GARCH model. We also elucidate why even higher-order GARCH models with Bollerslev's (1986) nonnegativity constraints enforced cannot mimic the long memory effects. The reasoning is supported with several empirical examples, for which we explicitly calculate the theoretical ACF implied by a couple of different fitted models, and find that their structure is just as predicted by our argument. To conveniently conduct these computations, a general simple method for computing the theoretical ACF of GARCH models is suggested, which is easier to use than the formulas developed so far, and particularly so for higher lag-orders. The ability of the component model to approximate long memory is also validated on the basis of a visual comparison between the empirical and the implied theoretical ACFs.