An Introduction to High-Frequency Finance
An Introduction to High-Frequency Finance

Author: Ramazan Gençay

Publisher: Elsevier

Published: 2001-05-29

Total Pages: 411

ISBN-13: 008049904X

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Liquid markets generate hundreds or thousands of ticks (the minimum change in price a security can have, either up or down) every business day. Data vendors such as Reuters transmit more than 275,000 prices per day for foreign exchange spot rates alone. Thus, high-frequency data can be a fundamental object of study, as traders make decisions by observing high-frequency or tick-by-tick data. Yet most studies published in financial literature deal with low frequency, regularly spaced data. For a variety of reasons, high-frequency data are becoming a way for understanding market microstructure. This book discusses the best mathematical models and tools for dealing with such vast amounts of data. This book provides a framework for the analysis, modeling, and inference of high frequency financial time series. With particular emphasis on foreign exchange markets, as well as currency, interest rate, and bond futures markets, this unified view of high frequency time series methods investigates the price formation process and concludes by reviewing techniques for constructing systematic trading models for financial assets.

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.

Econometrics of Financial High-Frequency Data
Econometrics of Financial High-Frequency Data

Author: Nikolaus Hautsch

Publisher: Springer Science & Business Media

Published: 2011-10-12

Total Pages: 381

ISBN-13: 364221925X

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The availability of financial data recorded on high-frequency level has inspired a research area which over the last decade emerged to a major area in econometrics and statistics. The growing popularity of high-frequency econometrics is driven by technological progress in trading systems and an increasing importance of intraday trading, liquidity risk, optimal order placement as well as high-frequency volatility. This book provides a state-of-the art overview on the major approaches in high-frequency econometrics, including univariate and multivariate autoregressive conditional mean approaches for different types of high-frequency variables, intensity-based approaches for financial point processes and dynamic factor models. It discusses implementation details, provides insights into properties of high-frequency data as well as institutional settings and presents applications to volatility and liquidity estimation, order book modelling and market microstructure analysis.

When Moving-Average Models Meet High-Frequency Data
When Moving-Average Models Meet High-Frequency Data

Author: Rui Da

Publisher:

Published: 2019

Total Pages: 107

ISBN-13:

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We propose uniformly valid inference on volatility with noisy high-frequency data. We assume the observed transaction price follows a continuous-time Itô-semimartingale, contaminated by a discrete-time moving-average noise process associated with the arrival of trades. We estimate the quadratic variation of the semimartingale by maximizing the likelihood of a misspecified moving-average model, with its order selected based on the information criteria. Our inference is uniformly valid over a large class of noise processes whose magnitude and dependence structure vary with sample size. Our implementation is tuning free barring order selection, and it yields positive estimates in finite samples. Finally, we provide consistent estimators of noise autocovariances as byproducts, which also play a critical role in achieving uniformity.

Inference for Time-Varying Lead-Lag Relationships from Ultra High Frequency Data
Inference for Time-Varying Lead-Lag Relationships from Ultra High Frequency Data

Author: Yuta Koike

Publisher:

Published: 2017

Total Pages: 40

ISBN-13:

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A new approach for modeling lead-lag relationships in high frequency financial markets is proposed. The model is accommodated to non-synchronous trading and market microstructure noise as well as intraday variations of lead-lag relationships, which are essential for empirical applications. A simple statistical methodology for analyzing the proposed model is presented as well. The methodology is illustrated by an empirical study to detect lead-lag relationships between the S&P 500 index and its two derivative products.

High Frequency Data, Frequency Domain Inference and Volatility Forecasting
High Frequency Data, Frequency Domain Inference and Volatility Forecasting

Author: Jonathan H. Wright

Publisher:

Published: 1999

Total Pages: 38

ISBN-13:

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While it is clear that the volatility of asset returns is serially correlated, there is no general agreement as to the most appropriate parametric model for characterizing this temporal dependence. In this paper, we propose a simple way of modeling financial market volatility using high frequency data. The method avoids using a tight parametric model, by instead simply fitting a long autoregression to log-squared, squared or absolute high frequency returns. This can either be estimated by the usual time domain method, or alternatively the autoregressive coefficients can be backed out from the smoothed periodogram estimate of the spectrum of log-squared, squared or absolute returns. We show how this approach can be used to construct volatility forecasts, which compare favorably with some leading alternatives in an out-of-sample forecasting exercise.