Prediction in ARMA models with GARCH in Mean Effects
AbstractThis paper considers forecasting the conditional mean and variance from an ARMA model with GARCH in mean effects. Expressions for the optimal predictors and their conditional and unconditional MSE's are presented. We also derive the formula for the covariance structure of the process and its conditional variance.
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 Department of Economics, University of York in its series Discussion Papers with number 99/11.
Date of creation:
Date of revision:
Contact details of provider:
Postal: Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom
Phone: (0)1904 323776
Fax: (0)1904 323759
Web page: http://www.york.ac.uk/economics/
More information through EDIRC
ARMA Model; Conditional Moments; GARCH in Mean Effects;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-1999-10-28 (All new papers)
- NEP-ECM-1999-10-28 (Econometrics)
- NEP-ETS-1999-10-28 (Econometric Time Series)
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.:
- Karanasos, M., 1998. "A New Method For Obtaining The Autocovariance Of An Arma Model: An Exact Form Solution," Econometric Theory, Cambridge University Press, vol. 14(05), pages 622-640, October.
- Christian Francq & Jean-Michel Zakoïan, 2013.
"Optimal predictions of powers of conditionally heteroscedastic processes,"
Journal of the Royal Statistical Society Series B,
Royal Statistical Society, vol. 75(2), pages 345-367, 03.
- Christan Francq & Jean-Michel Zakoian, 2012. "Optimal Predictions of Powers of Conditionally Heteroskedastic Processes," Working Papers 2012-17, Centre de Recherche en Economie et Statistique.
- Francq, Christian & Zakoian, Jean-Michel, 2010. "Optimal predictions of powers of conditionally heteroskedastic processes," MPRA Paper 22155, University Library of Munich, Germany.
- Stilianos Fountas & Menelaos Karanasos & Marika Karanassou, 2000.
"A GARCH Model of Inflation and Inflation Uncertainty with Simultaneous Feedback,"
414, Queen Mary, University of London, School of Economics and Finance.
- Stilianos Fountas & Menelaos Karanasos & Marika Karanassou, 2000. "A GARCH Model of Inflation and Inflation Uncertainty with Simultaneous Feedback," Working Papers 0047, National University of Ireland Galway, Department of Economics, revised 2000.
- Stilianos Fountas & Menelaos Karanasos & Marika Karanassou, . "A GARCH Model of Inflation and Inflation Uncertainty with Simultaneous Feedback," Discussion Papers 00/24, Department of Economics, University of York.
- Menelaos Karanasos, . "The Covariance Structure of Component and Multivariate Garch Models," Discussion Papers 99/12, Department of Economics, University of York.
- Hlouskova, Jaroslava & Schmidheiny, Kurt & Wagner, Martin, 2009.
"Multistep predictions for multivariate GARCH models: Closed form solution and the value for portfolio management,"
Journal of Empirical Finance,
Elsevier, vol. 16(2), pages 330-336, March.
- Jaroslava HLOUSKOVA & Kurt SCHMIDHEINY & Martin WAGNER, 2004. "Multistep Predictions for Multivariate GARCH Models: Closed Form Solution and the Value for Portfolio Management," Cahiers de Recherches Economiques du DÃ©partement d'EconomÃ©trie et d'Economie politique (DEEP) 04.10, Université de Lausanne, Faculté des HEC, DEEP.
- Menelaos Karanasos & J. Kim, . "Alternative GARCH in Mean Models: An Application to the Korean Stock Market," Discussion Papers 00/25, Department of Economics, University of York.
- Menelaos Karanasos, . "Some Exact Formulae for the Constant Correlation and Diagonal M - Garch Models," Discussion Papers 00/14, Department of Economics, University of York.
- De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
- Christian Conrad & Enno Mammen, 2008. "Nonparametric Regression on Latent Covariates with an Application to Semiparametric GARCH-in-Mean Models," Working Papers 0473, University of Heidelberg, Department of Economics, revised Jul 2008.
- Menelaos Karanasos, .
"The Covariance Structure of Mixed ARMA Models,"
00/11, Department of Economics, University of York.
- Jaroslava Hlouskova & Kurt Schmidheiny & Martin Wagner, 2002. "Multistep Predictions from Multivariate ARMA-GARCH: Models and their Value for Portfolio Management," Diskussionsschriften dp0212, Universitaet Bern, Departement Volkswirtschaft.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Paul Hodgson).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.