IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v10y2003i4p251-254.html
   My bibliography  Save this article

k -Factor GARMA models for intraday volatility forecasting

Author

Listed:
  • Luisa Bisaglia
  • Silvano Bordignon
  • Francesco Lisi

Abstract

This paper studies the ability of the k -factor GARMA processes to model and forecast the volatility of an intraday financial time series. Forecasting results from the k -factor GARMA model are obtained and compared with those produced by a conventional SARIMA model.

Suggested Citation

  • Luisa Bisaglia & Silvano Bordignon & Francesco Lisi, 2003. "k -Factor GARMA models for intraday volatility forecasting," Applied Economics Letters, Taylor & Francis Journals, vol. 10(4), pages 251-254.
  • Handle: RePEc:taf:apeclt:v:10:y:2003:i:4:p:251-254
    DOI: 10.1080/1350485032000050653
    as

    Download full text from publisher

    File URL: http://www.informaworld.com/openurl?genre=article&doi=10.1080/1350485032000050653&magic=repec&7C&7C8674ECAB8BB840C6AD35DC6213A474B5
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/1350485032000050653?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    2. 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.
    3. Dacorogna, Michael M. & Muller, Ulrich A. & Nagler, Robert J. & Olsen, Richard B. & Pictet, Olivier V., 1993. "A geographical model for the daily and weekly seasonal volatility in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 12(4), pages 413-438, August.
    4. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    5. Luisa Bisaglia & Silvano Bordignon, 2002. "Mean square prediction error for long-memory processes," Statistical Papers, Springer, vol. 43(2), pages 161-175, April.
    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. Leschinski, Christian & Sibbertsen, Philipp, 2014. "Model Order Selection in Seasonal/Cyclical Long Memory Models," Hannover Economic Papers (HEP) dp-535, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. Dissanayake, G.S. & Peiris, M.S. & Proietti, T., 2016. "State space modeling of Gegenbauer processes with long memory," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 115-130.
    3. Guglielmo Maria Caporale & Luis Gil‐Alana, 2014. "Long‐Run and Cyclical Dynamics in the US Stock Market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(2), pages 147-161, March.
    4. Silvano Bordignon & Massimiliano Caporin & Francesco Lisi, 2009. "Periodic Long-Memory GARCH Models," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 60-82.
    5. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    6. Proietti, Tommaso & Maddanu, Federico, 2024. "Modelling cycles in climate series: The fractional sinusoidal waveform process," Journal of Econometrics, Elsevier, vol. 239(1).
    7. Alex Gonzaga & Michael Hauser, 2011. "A wavelet Whittle estimator of generalized long-memory stochastic volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 23-48, March.
    8. Bordignon, Silvano & Caporin, Massimiliano & Lisi, Francesco, 2007. "Generalised long-memory GARCH models for intra-daily volatility," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5900-5912, August.
    9. McElroy, Tucker S. & Holan, Scott H., 2016. "Computation of the autocovariances for time series with multiple long-range persistencies," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 44-56.
    10. Alva, Kenedy, 2009. "Modelling intra-daily volatility by functional data analysis: an empirical application to the spanish stock market," DES - Working Papers. Statistics and Econometrics. WS ws092809, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Leschinski, Christian & Sibbertsen, Philipp, 2019. "Model order selection in periodic long memory models," Econometrics and Statistics, Elsevier, vol. 9(C), pages 78-94.
    12. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

    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. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," PIER Working Paper Archive 03-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Sep 2003.
    2. Galbraith, John W. & KI[#x1e63]Inbay, Turgut, 2005. "Content horizons for conditional variance forecasts," International Journal of Forecasting, Elsevier, vol. 21(2), pages 249-260.
    3. Darmoul Mokhtar & Nizar Harrathi, 2007. "Monetary information arrivals and intraday exchange rate volatility: a comparison of the GARCH and the EGARCH models," Post-Print halshs-00174996, HAL.
    4. Ballocchi, Giuseppe & Dacorogna, Michel M. & Hopman, Carl M. & Muller, Ulrich A. & Olsen, Richard B., 1999. "The intraday multivariate structure of the Eurofutures markets," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 479-513, December.
    5. Ibrahim Chowdhury & Lucio Sarno, 2004. "Time‐Varying Volatility in the Foreign Exchange Market: New Evidence on its Persistence and on Currency Spillovers," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(5‐6), pages 759-793, June.
    6. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    7. repec:uts:finphd:39 is not listed on IDEAS
    8. John Cotter & Simon Stevenson, 2008. "Modeling Long Memory in REITs," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 36(3), pages 533-554, September.
    9. Kang, Sang Hoon & Yoon, Seong-Min, 2008. "Long memory features in the high frequency data of the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5189-5196.
    10. Dominguez, Kathryn M.E., 2006. "When do central bank interventions influence intra-daily and longer-term exchange rate movements?," Journal of International Money and Finance, Elsevier, vol. 25(7), pages 1051-1071, November.
    11. Takatoshi Ito & Richard K. Lyons & Michael T. Melvin, 1996. "Is There Private Information in the FX Market? The Tokyo Experiment," Working Papers _005, University of California at Berkeley, Haas School of Business.
    12. Beltratti, Andrea & Morana, Claudio, 1999. "Computing value at risk with high frequency data," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 431-455, December.
    13. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    14. Y. -F. Gau & M. Hau, 2004. "Public information, private information, inventory control, and volatility of intraday NTD/USD exchange rates," Applied Economics Letters, Taylor & Francis Journals, vol. 11(4), pages 263-266.
    15. Gil-Alana, Luis A. & Shittu, Olanrewaju I. & Yaya, OlaOluwa S., 2014. "On the persistence and volatility in European, American and Asian stocks bull and bear markets," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 149-162.
    16. Young Wook Han, 2010. "The Effects of US Macroeconomic Surprises on the Intraday Movements of Foreign Exchange Rates: Cases of USD-EUR and USD-JPY Exchange Rates," International Economic Journal, Taylor & Francis Journals, vol. 24(3), pages 375-396.
    17. Melvin, Michael & Yin, Xixi, 2000. "Public Information Arrival, Exchange Rate Volatility, and Quote Frequency," Economic Journal, Royal Economic Society, vol. 110(465), pages 644-661, July.
    18. Borus Jungbacker & Siem Jan Koopman, 2006. "Model-Based Measurement of Actual Volatility in High-Frequency Data," Advances in Econometrics, in: Econometric Analysis of Financial and Economic Time Series, pages 183-210, Emerald Group Publishing Limited.
    19. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    20. repec:uts:finphd:38 is not listed on IDEAS
    21. Evans, Kevin P. & Speight, Alan E.H., 2010. "Intraday periodicity, calendar and announcement effects in Euro exchange rate volatility," Research in International Business and Finance, Elsevier, vol. 24(1), pages 82-101, January.
    22. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-059, New York University, Leonard N. Stern School of Business-.

    More about this item

    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:taf:apeclt:v:10:y:2003:i:4:p:251-254. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    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.