Forecasting Irregularly Spaced UHF Financial Data: Realized Volatility vs UHF-GARCH Models
AbstractA very promising literature has been recently devoted to the modeling of ultra-high-frequency (UHF) data. Our first aim is to develop an empirical application of Autoregressive Conditional Duration GARCH models and the realized volatility to forecast future volatilities on irregularly spaced data. We also compare the out sample performances of ACD GARCH models with the realized volatility method. We propose a procedure to take into account the time deformation and show how to use these models for computing daily VaR.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Département des sciences administratives, UQO in its series RePAd Working Paper Series with number UQO-DSA-wp152006.
Length: 27 pages
Date of creation: 06 Jul 2006
Date of revision:
Contact details of provider:
Postal: Pavillon Lucien Brault, 101 rue Saint Jean-Bosco, Gatineau (Québec) J8Y 3G5
Phone: (819) 595-3900
Fax: (819) 773-1747
Web page: http://www.repad.org/
More information through EDIRC
Realized volatility; Ultra High Frequency GARCH; time deformation; financial markets; Daily VaR.;
Other versions of this item:
- François-Éric Racicot & Raymond Théoret & Alain Coën, 2008. "Forecasting Irregularly Spaced UHF Financial Data: Realized Volatility vs UHF-GARCH Models," International Advances in Economic Research, Springer, vol. 14(1), pages 112-124, February.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-07-15 (All new papers)
- NEP-BEC-2006-07-15 (Business Economics)
- NEP-ECM-2006-07-15 (Econometrics)
- NEP-ETS-2006-07-15 (Econometric Time Series)
- NEP-FIN-2006-07-15 (Finance)
- NEP-FMK-2006-07-15 (Financial Markets)
- NEP-FOR-2006-07-15 (Forecasting)
- NEP-MST-2006-07-15 (Market Microstructure)
- NEP-RMG-2006-07-15 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ole E. Barndorff-Nielsen & Neil Shephard, 2002.
"Estimating quadratic variation using realized variance,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
- Neil Shephard & Ole E. Barndorff-Nielsen, 2002. "Estimating quadratic variation using realised variance," Economics Series Working Papers 2001-W20, University of Oxford, Department of Economics.
- Jonathan H. Wright & Tim Bollerslev, 1999.
"High frequency data, frequency domain inference and volatility forecasting,"
International Finance Discussion Papers
649, Board of Governors of the Federal Reserve System (U.S.).
- Tim Bollerslev & Jonathan H. Wright, 2001. "High-Frequency Data, Frequency Domain Inference, And Volatility Forecasting," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 596-602, November.
- Meddahi, N., 2001.
"A Theoretical Comparison Between Integrated and Realized Volatilies,"
Cahiers de recherche
2001-26, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Nour Meddahi, 2002. "A theoretical comparison between integrated and realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 479-508.
- MEDDAHI, Nour, 2001. "A Theoretical Comparison Between Integrated and Realized Volatilies," Cahiers de recherche 2001-26, Universite de Montreal, Departement de sciences economiques.
- Gourieroux, Christian & Jasiak, Joanna & Le Fol, Gaelle, 1999.
"Intra-day market activity,"
Journal of Financial Markets,
Elsevier, vol. 2(3), pages 193-226, August.
- Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
- Nour Meddahi, 2003.
"ARMA representation of integrated and realized variances,"
Royal Economic Society, vol. 6(2), pages 335-356, December.
- Nour Meddahi, 2002. "ARMA Representation of Integrated and Realized Variances," CIRANO Working Papers 2002s-93, CIRANO.
- MEDDAHI, Nour, 2002. "ARMA Representation of Integrated and Realized Variances," Cahiers de recherche 2002-20, Universite de Montreal, Departement de sciences economiques.
- Easley, David & O'Hara, Maureen, 1992. " Time and the Process of Security Price Adjustment," Journal of Finance, American Finance Association, vol. 47(2), pages 576-605, June.
- Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, vol. 4(1), pages 17-46, January.
- Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
- Andersen, Torben G. & Bollerslev, Tim & Lange, Steve, 1999. "Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 457-477, December.
- Robert F. Engle, 1996.
"The Econometrics of Ultra-High Frequency Data,"
NBER Working Papers
5816, National Bureau of Economic Research, Inc.
- Bollerslev, Tim & Zhang, Benjamin Y. B., 2003. "Measuring and modeling systematic risk in factor pricing models using high-frequency data," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 533-558, December.
- Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 1-46 National Bureau of Economic Research, Inc.
- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Ole E. Barndorff-Nielsen & Neil Shephard, 2000.
"Econometric analysis of realised volatility and its use in estimating stochastic volatility models,"
2001-W4, Economics Group, Nuffield College, University of Oxford, revised 05 Jul 2001.
- Ole E. Barndorff-Nielsen & Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280.
- Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
- Josifidis, Kosta & Allegret, Jean-Pierre & Gimet, Céline & Pucar, Emilija Beker, 2014. "Macroeconomic policy responses to financial crises in emerging European economies," Economic Modelling, Elsevier, vol. 36(C), pages 577-591.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christian Calmes).
If references are entirely missing, you can add them using this form.