Time Series Forecasting of Volatility Using High Frequency Data
Author : Hai Kang Tan
Publisher :
Page : 25 pages
File Size : 20,40 MB
Release : 2002
Category :
ISBN :
High Frequency Data, Frequency Domain Inference and Volatility Forecasting
Author : Jonathan H. Wright
Publisher :
Page : 38 pages
File Size : 14,81 MB
Release : 1999
Category : Rate of return
ISBN :
Book Description
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.
Modelling and Forecasting High Frequency Financial Data
Author : Stavros Degiannakis
Publisher : Springer
Page : 301 pages
File Size : 26,36 MB
Release : 2016-04-29
Category : Business & Economics
ISBN : 1137396490
Book Description
The global financial crisis has reopened discussion surrounding the use of appropriate theoretical financial frameworks to reflect the current economic climate. There is a need for more sophisticated analytical concepts which take into account current quantitative changes and unprecedented turbulence in the financial markets. This book provides a comprehensive guide to the quantitative analysis of high frequency financial data in the light of current events and contemporary issues, using the latest empirical research and theory. It highlights and explains the shortcomings of theoretical frameworks and provides an explanation of high-frequency theory, emphasising ways in which to critically apply this knowledge within a financial context. Modelling and Forecasting High Frequency Financial Data combines traditional and updated theories and applies them to real-world financial market situations. It will be a valuable and accessible resource for anyone wishing to understand quantitative analysis and modelling in current financial markets.
Forecasting Volatility Using High Frequency Data
Author : Peter Reinhard Hansen
Publisher :
Page : 37 pages
File Size : 16,8 MB
Release : 2018
Category :
ISBN :
Book Description
Handbook chapter on volatility forecasting using high-frequency data, with surveys of reduced-form volatility forecasts and model-based volatility forecasts.
The Oxford Handbook of Economic Forecasting
Author : Michael P. Clements
Publisher : OUP USA
Page : 732 pages
File Size : 20,36 MB
Release : 2011-07-08
Category : Business & Economics
ISBN : 0195398645
Book Description
Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
An Introduction to High-Frequency Finance
Author : Ramazan Gençay
Publisher : Elsevier
Page : 411 pages
File Size : 35,14 MB
Release : 2001-05-29
Category : Business & Economics
ISBN : 008049904X
Book Description
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
Author : Yacine Aït-Sahalia
Publisher : Princeton University Press
Page : 683 pages
File Size : 25,19 MB
Release : 2014-07-21
Category : Business & Economics
ISBN : 0691161437
Book Description
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.
Econometric Forecasting And High-frequency Data Analysis
Author : Yiu-kuen Tse
Publisher : World Scientific
Page : 200 pages
File Size : 39,50 MB
Release : 2008-03-04
Category : Business & Economics
ISBN : 9814472360
Book Description
This important book consists of surveys of high-frequency financial data analysis and econometric forecasting, written by pioneers in these areas including Nobel laureate Lawrence Klein. Some of the chapters were presented as tutorials to an audience in the Econometric Forecasting and High-Frequency Data Analysis Workshop at the Institute for Mathematical Science, National University of Singapore in May 2006. They will be of interest to researchers working in macroeconometrics as well as financial econometrics. Moreover, readers will find these chapters useful as a guide to the literature as well as suggestions for future research.
Predicting Financial Volatility
Author : Martin Martens
Publisher :
Page : 0 pages
File Size : 10,61 MB
Release : 2008
Category :
ISBN :
Book Description
Recent evidence suggests option implied volatility provides better forecasts of financial volatility than time-series models based on historical daily returns. In particular it is found that daily GARCH forecasts have no or little incremental information over that already contained in implied volatilities. In this study both the measurement and the forecasting of financial volatility is improved using high-frequency data and the latest proposed model for volatility, a long memory model. The results indicate that volatility forecasts based on historical intraday returns do provide good volatility forecasts that can compete with implied volatility and sometimes even outperform implied volatility.
Forecasting High-Frequency Volatility Shocks
Author : Holger Kömm
Publisher : Springer
Page : 188 pages
File Size : 25,79 MB
Release : 2016-02-08
Category : Business & Economics
ISBN : 3658125969
Book Description
This thesis presents a new strategy that unites qualitative and quantitative mass data in form of text news and tick-by-tick asset prices to forecast the risk of upcoming volatility shocks. Holger Kömm embeds the proposed strategy in a monitoring system, using first, a sequence of competing estimators to compute the unobservable volatility; second, a new two-state Markov switching mixture model for autoregressive and zero-inflated time-series to identify structural breaks in a latent data generation process and third, a selection of competing pattern recognition algorithms to classify the potential information embedded in unexpected, but public observable text data in shock and nonshock information. The monitor is trained, tested, and evaluated on a two year survey on the prime standard assets listed in the indices DAX, MDAX, SDAX and TecDAX.
