On the autocorrelation properties of Long Memory Garch Processes
AbstractThis paper derives the autocorrelation function of the squared values of long-memory GARCH processes. The latter are of much interest since they can produce the long-memory conditional heteroscedasticity that many high-frequency financial time series exhibit. An empirical application illustrating the practical use of our results is also discussed.
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 Universidad Torcuato Di Tella in its series Department of Economics Working Papers with number 025.
Length: 15 pages
Date of creation: May 2002
Date of revision:
Contact details of provider:
Web page: http://www.utdt.edu/ver_contenido.php?id_contenido=439&id_item_menu=568
More information through EDIRC
Autocorrelation function; Fractionally integrated GARCH process; Long-memory GARCH process.;
Other versions of this item:
- Menelaos Karanasos & Zacharias Psaradakis & Martin Sola, 2004. "On the Autocorrelation Properties of Long-Memory GARCH Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 265-282, 03.
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Conrad, Christian, 2010.
"Non-negativity conditions for the hyperbolic GARCH model,"
Journal of Econometrics,
Elsevier, vol. 157(2), pages 441-457, August.
- Christian Conrad, 2007. "Non-negativity Conditions for the Hyperbolic GARCH Model," KOF Working papers 07-162, KOF Swiss Economic Institute, ETH Zurich.
- Christian Conrad & Menelaos Karanasos & Ning Zeng, 2008.
"Multivariate Fractionally Integrated APARCH Modeling of Stock Market Volatility: A multi-country study,"
0472, University of Heidelberg, Department of Economics, revised Jul 2008.
- Conrad, Christian & Karanasos, Menelaos & Zeng, Ning, 2011. "Multivariate fractionally integrated APARCH modeling of stock market volatility: A multi-country study," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 147-159, January.
- Adnen Ben Nasr & Mohamed Boutahar & Abdelwahed Trabelsi, 2010. "Fractionally integrated time varying GARCH model," Statistical Methods and Applications, Springer, vol. 19(3), pages 399-430, August.
- Richard T. Baillie & Claudio Morana, 2007. "Modeling Long Memory and Structural Breaks in Conditional Variances: An Adaptive FIGARCH Approach," Working Papers 593, Queen Mary, University of London, School of Economics and Finance.
- Baillie, Richard T. & Morana, Claudio, 2009.
"Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach,"
Journal of Economic Dynamics and Control,
Elsevier, vol. 33(8), pages 1577-1592, August.
- Richard T. Baillie & Claudio Morana, 2007. "Modeling Long Memory and Structural Breaks in Conditional Variances: an Adaptive FIGARCH Approach," ICER Working Papers - Applied Mathematics Series 11-2007, ICER - International Centre for Economic Research.
- Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.
- Adnen Ben Nasr & Ahdi N. Ajmi & Rangan Gupta, 2013. "Modeling the Volatility of the Dow Jones Islamic Market World Index Using a Fractionally Integrated Time Varying GARCH (FITVGARCH) Model," Working Papers 201357, University of Pretoria, Department of Economics.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Martin Cecilia Lafuente).
If references are entirely missing, you can add them using this form.