IDEAS home Printed from https://ideas.repec.org/p/yor/yorken/99-11.html
   My bibliography  Save this paper

Prediction in ARMA models with GARCH in Mean Effects

Author

Listed:
  • Menelaos Karanasos

Abstract

This 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.

Suggested Citation

  • Menelaos Karanasos, "undated". "Prediction in ARMA models with GARCH in Mean Effects," Discussion Papers 99/11, Department of Economics, University of York.
    • Handle: RePEc:yor:yorken:99/11
    as

    Download full text from publisher

    File URL: https://www.york.ac.uk/media/economics/documents/discussionpapers/1999/9911.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    2. 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(5), pages 622-640, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Menelaos Karanasos, "undated". "The Covariance Structure of Mixed ARMA Models," Discussion Papers 00/10, Department of Economics, University of York.
    2. Antonis Demos, 2002. "Moments and dynamic structure of a time-varying parameter stochastic volatility in mean model," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 345-357, June.
    3. Haiyan Zhao & Fred Huffer & Xu-Feng Niu, 2015. "Time-varying coefficient models with ARMA-GARCH structures for longitudinal data analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(2), pages 309-326, February.
    4. Stilianos Fountas & Menelaos Karanasos & Marika Karanassou, "undated". "A GARCH Model of Inflation and Inflation Uncertainty with Simultaneous Feedback," Discussion Papers 00/24, Department of Economics, University of York.
    5. Carol Alexander & Emese Lazar & Silvia Stanescu, 2010. "Analytic Moments for GARCH Processes," ICMA Centre Discussion Papers in Finance icma-dp2011-07, Henley Business School, University of Reading, revised Apr 2011.
    6. repec:awi:wpaper:0473 is not listed on IDEAS
    7. 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.
    8. 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.
    9. 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.
    10. 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, March.
    11. Karanasos, Menelaos & Paraskevopoulos,Alexandros & Canepa, Alessandra, 2020. "Unified Theory for the Large Family of Time Varying Models with Arma Representations: One Solution Fits All," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202008, University of Turin.
    12. Rong Xu & Xingye Li, 2017. "Study About the Minimum Value at Risk of Stock Index Futures Hedging Applying Exponentially Weighted Moving Average - Generalized Autoregressive Conditional Heteroskedasticity Model," International Journal of Economics and Financial Issues, Econjournals, vol. 7(6), pages 104-110.
    13. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    14. Menelaos Karanasos, "undated". "The Covariance Structure of Component and Multivariate Garch Models," Discussion Papers 99/12, Department of Economics, University of York.
    15. Menelaos Karanasos, "undated". "Some Exact Formulae for the Constant Correlation and Diagonal M - Garch Models," Discussion Papers 00/14, Department of Economics, University of York.
    16. M. Karanasos & J. Kim, 2003. "Moments of the ARMA--EGARCH model," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 146-166, June.
    17. Menelaos Karanasos & Alexandros Paraskevopoulos & Faek Menla Ali & Michail Karoglou & Stavroula Yfanti, 2014. "Modelling Returns and Volatilities During Financial Crises: a Time Varying Coefficient Approach," Papers 1403.7179, arXiv.org.
    18. Stilianos Fountas & Menelaos Karanasos & Marika Karanassou, "undated". "A GARCH Model of Inflation and Inflation Uncertainty with Simultaneous Feedback," Discussion Papers 00/24, Department of Economics, University of York.
    19. Menelaos Karanasos & J. Kim, "undated". "Alternative GARCH in Mean Models: An Application to the Korean Stock Market," Discussion Papers 00/25, Department of Economics, University of York.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Menelaos Karanasos, "undated". "The Covariance Structure of Mixed ARMA Models," Discussion Papers 00/11, Department of Economics, University of York.
    2. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    3. Erie Febrian & Aldrin Herwany, 2009. "Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets," Working Papers in Economics and Development Studies (WoPEDS) 200911, Department of Economics, Padjadjaran University, revised Sep 2009.
    4. David Murrell & Weiqiu Yu, 2000. "The Effect of the Harmonized Sales Tax on Consumer Prices in Atlantic Canada," Canadian Public Policy, University of Toronto Press, vol. 26(4), pages 451-460, December.
    5. Thomas Dohmen & Hartmut F. Lehmann & Mark E. Schaffer, 2014. "Wage Policies of a Russian Firm and the Financial Crisis of 1998: Evidence from Personnel Data, 1997 to 2002," ILR Review, Cornell University, ILR School, vol. 67(2), pages 504-531, April.
    6. Jun Ma & Mark E. Wohar, 2013. "An Unobserved Components Model that Yields Business and Medium-Run Cycles," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(7), pages 1351-1373, October.
    7. Pär Österholm, 2005. "The Taylor Rule: A Spurious Regression?," Bulletin of Economic Research, Wiley Blackwell, vol. 57(3), pages 217-247, July.
    8. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    9. Ang, Andrew & Piazzesi, Monika & Wei, Min, 2006. "What does the yield curve tell us about GDP growth?," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 359-403.
    10. Michaelides, Panayotis & Milios, John, 2009. "TFP change, output gap and inflation in the Russian Federation (1994-2006)," Journal of Economics and Business, Elsevier, vol. 61(4), pages 339-352, July.
    11. Babula, Ronald A., 1997. "Economic Effects Of A Countervailing Duty Order On The U.S. Lamb Meat Industry," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 26(1), pages 1-12, April.
    12. Giancarlo Lutero & Marco Marini, 2010. "Direct vs Indirect Forecasts of Foreign Trade Unit Value Indices," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 12(2-3), pages 73-96, October.
    13. jose ramos pires manso, 2004. "Economical Versus Political Cycles In An Iberian Manufacturing Sector," Industrial Organization 0404003, University Library of Munich, Germany.
    14. Chakrabarti, Avik, 2006. "The saving-investment relationship revisited: New evidence from multivariate heterogeneous panel cointegration analyses," Journal of Comparative Economics, Elsevier, vol. 34(2), pages 402-419, June.
    15. Baghestani, Hamid, 2008. "Federal Reserve versus private information: Who is the best unemployment rate predictor," Journal of Policy Modeling, Elsevier, vol. 30(1), pages 101-110.
    16. Parigi, Giuseppe & Golinelli, Roberto, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
    17. Aris Spanos & Niki Papadopoulou, 2013. "A Small Macroeconometric Model for the Cyprus Economy," Working Papers 2013-02, Central Bank of Cyprus.
    18. Iftekhar Hasan & Heiko Schmiedel & Liang Song, 2012. "Returns to Retail Banking and Payments," Journal of Financial Services Research, Springer;Western Finance Association, vol. 41(3), pages 163-195, June.
    19. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    20. Ceci, Vladimiro & Manganelli, Simone & Vecchiato, Walter, 2002. "Sensitivity analysis of volatility: a new tool for risk management," Working Paper Series 194, European Central Bank.

    More about this item

    Keywords

    ARMA Model; Conditional Moments; GARCH in Mean Effects;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:yor:yorken:99/11. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Paul Hodgson (email available below). General contact details of provider: https://edirc.repec.org/data/deyoruk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.