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.
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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
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Web page: http://www.utdt.edu/ver_contenido.php?id_contenido=439&id_item_menu=568
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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.
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- Conrad, Christian & Karanasos, Menelaos & Zeng, Ning, 2011.
"Multivariate fractionally integrated APARCH modeling of stock market volatility: A multi-country study,"
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- Christian Conrad & Menelaos Karanasos & Ning Zeng, 2008. "Multivariate Fractionally Integrated APARCH Modeling of Stock Market Volatility: A multi-country study," Working Papers 0472, University of Heidelberg, Department of Economics, revised Jul 2008.
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- Axel Groß‐KlußMann & Nikolaus Hautsch, 2013. "Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(8), pages 724-742, December.
- Adnen Ben Nasr & Ahdi N. Ajmi & Rangan Gupta, 2013.
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201357, University of Pretoria, Department of Economics.
- Adnen Ben Nasr & Ahdi Noomen Ajmi & Rangan Gupta, 2014. "Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model," Applied Financial Economics, Taylor & Francis Journals, vol. 24(14), pages 993-1004, July.
- 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.
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