Modeling and Forecasting of Intraday Volatility PDF Download

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 76

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Author: Abhay Kumar Singh
Publisher:
ISBN:
Category :
Languages : en
Pages : 25

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On the afternoon of May 6, 2010 the Dow Jones Industrial Average (DJIA) plunged about 1000 points (about 9%) in a matter of minutes before rebounding almost as quickly. This was the biggest one day point decline on an intraday basis in the DJIA's history. An almost similar dramatic change in intraday volatility was observed on April 4, 2000 when the DJIA dropped by 4.8%. These historical events present a very compelling argument for the need for robust econometrics models which can forecast intraday asset volatility. There are numerous models available in the finance literature to model financial asset volatility. Various Autoregressive Conditional Heteroskedastic (ARCH) time series models are widely used for modelling daily (end of day) volatility of the financial assets. The family of basic GARCH models works well for modelling daily volatility but they are proven to be not as efficient for intraday volatility. The last two decades have seen some research augmenting the GARCH family of models to forecast intraday volatility, the Multiplicative Component GARCH (MCGARCH) model of Engle & Sokalska (2012) being the most recent of them. MCGARCH models the conditional variance as the multiplicative product of daily, diurnal, and stochastic intraday volatility of the financial asset. In this paper we use the MCGARCH model to forecast the intraday volatility of Australia's S&P/ASX-50 stock market index and the USA Dow Jones Industrial Average (DJIA) stock market index. We also use the model to forecast their intraday Value at Risk (VaR) and Expected Shortfall (ES). As the model requires a daily volatility component, we test a GARCH based estimate of the daily volatility component against the daily realized volatility (RV) estimates obtained from the Heterogeneous Autoregressive model for Realized Volatility (HARRV). The results in the paper show that 1 minute VaR forecasts obtained from the MCGARCH model using the HARRV based daily volatility component outperform the ones obtained using the GARCH based daily volatility component.

Author: Xuna Gao
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 162

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The purpose of this study is to investigate stock volatility and forecasting performance of different volatility models over high-frequency intervals. The multiplicative component model that decomposes the conditional variance into a daily component and a periodicity component is studied with different specifications. This model is applied to 30 stocks. For the daily component, both parametric and non-parametric measures are considered. 12 models that capture the long memory feature of volatility are examined. Our results show the HAR-MEM model with overnight jump and the HAR-MEM model have the best forecasting performance among 12 models, and adding an overnight return term improves model's forecasting ability. Periodicity component is captured by the proportion of summation of intraday volatility to summation of daily volatility over some time period. In comparison with the literature, our specification of periodicity component has slightly better forecasting performance in the first 2-hour volatility.

Author: Michiel de Pooter
Publisher: Rozenberg Publishers
ISBN: 9051709153
Category :
Languages : en
Pages : 286

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This dissertation consists of a collection of studies on two areas in quantitative finance: asset return volatility and the term structure of interest rates. The first part of this dissertation offers contributions to the literature on how to test for sudden changes in unconditional volatility, on modelling realized volatility and on the choice of optimal sampling frequencies for intraday returns. The emphasis in the second part of this dissertation is on the term structure of interest rates.

Author: Stavros Degiannakis
Publisher: Springer
ISBN: 1137396490
Category : Business & Economics
Languages : en
Pages : 411

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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.

Author: Stephen Satchell
Publisher: Elsevier
ISBN: 0080471420
Category : Business & Economics
Languages : en
Pages : 428

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Forecasting Volatility in the Financial Markets, Third Edition assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting-edge modelling and forecasting techniques. It provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding of volatility measures and risk management strategies will benefit from this collection of up-to-date chapters on the latest techniques in forecasting volatility. Chapters new to this third edition:* What good is a volatility model? Engle and Patton* Applications for portfolio variety Dan diBartolomeo* A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices Rob Cornish* Volatility modeling and forecasting in finance Xiao and Aydemir* An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility Thomas A. Silvey Leading thinkers present newest research on volatility forecasting International authors cover a broad array of subjects related to volatility forecasting Assumes basic knowledge of volatility, financial mathematics, and modelling

Author: Eugenie M.J.H. Hol
Publisher: Springer Science & Business Media
ISBN: 147575129X
Category : Business & Economics
Languages : en
Pages : 168

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Empirical Studies on Volatility in International Stock Markets describes the existing techniques for the measurement and estimation of volatility in international stock markets with emphasis on the SV model and its empirical application. Eugenie Hol develops various extensions of the SV model, which allow for additional variables in both the mean and the variance equation. In addition, the forecasting performance of SV models is compared not only to that of the well-established GARCH model but also to implied volatility and so-called realised volatility models which are based on intraday volatility measures. The intended readers are financial professionals who seek to obtain more accurate volatility forecasts and wish to gain insight about state-of-the-art volatility modelling techniques and their empirical value, and academic researchers and students who are interested in financial market volatility and want to obtain an updated overview of the various methods available in this area.

Author: Xingyi Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 33

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There is evidence that volatility forecasting models that use intraday data provide better forecast accuracy as compared with that delivered by the models that use daily data. Exactly how much better is still unknown. The present paper fills this gap in the literature and extends previous studies on forecasting stock market volatility in several important directions. First, we employ an extensive set of intraday data on 31 individual stocks over a sample period of 19 years. Second, we use forecast horizons ranging from 1 day to 6 months. Third, we evaluate the precision of volatility forecast provided by various competing models. Fourth, we conduct several robustness checks to assess the sensitivity of our results to various alternative choices. The major finding of our empirical study is that the gains from using intraday data are rather significant and persist over longer forecast horizons. Depending on the forecast horizon, the improvement in forecast precision varies from 30 to 50 percent. We demonstrate that our main results on the forecast accuracy gains are robust to the choice of intraday data frequency and the choice of measure of realized daily volatility.

Author: John L. Knight
Publisher: Butterworth-Heinemann
ISBN: 9780750655156
Category : Business & Economics
Languages : en
Pages : 428

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This text assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting edge modeling and forecasting techniques. It then uses a technical survey to explain the different ways to measure risk and define the different models of volatility and return.

Author: Luc Bauwens
Publisher: John Wiley & Sons
ISBN: 1118272056
Category : Business & Economics
Languages : en
Pages : 566

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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.