Advanced Search
MyIDEAS: Login to save this paper or follow this series

Doubly fractional models for dynamic heteroskedastic cycles

Contents:

Author Info

  • Arteche González, Jesús María
  • Artiach Escauriaza, Miguel Manuel

Abstract

Strong persistence is a common phenomenon that has been documented not only in the levels but also in the volatility of many time series. The class of doubly fractional models is extended to include the possibility of long memory in cyclical (non-zero) frequencies in both the levels and the volatility and a new model, the GARMA-GARMASV (Gegenbauer AutoRegressive Mean Average - Id. Stochastic Volatility) is introduced. A sequential estimation strategy, based on the Whittle approximation to maximum likelihood is proposed and its finite sample performance is evaluated with a Monte Carlo analysis. Finally, a trifactorial in the mean and bifactorial in the volatility version of the model is proved to successfully fit the well-known sunspot index.

Download Info

If 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.
File URL: https://addi.ehu.es/bitstream/10810/5577/1/2011.03.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística) in its series BILTOKI with number 2011-03.

as in new window
Length:
Date of creation: Feb 2011
Date of revision:
Handle: RePEc:ehu:biltok:201103

Contact details of provider:
Postal: Avda. Lehendakari, Aguirre, 83, 48015 Bilbao
Phone: + 34 94 601 3740
Fax: + 34 94 601 4935
Email:
Web page: http://www.ea3.ehu.es
More information through EDIRC

Order Information:
Postal: Dpto. de Econometría y Estadística, Facultad de CC. Económicas y Empresariales, Universidad del País Vasco, Avda. Lehendakari Aguirre 83, 48015 Bilbao, Spain
Email:

Related research

Keywords: long memory; stochastic volatility; cycles; QML estimation; sunspot index;

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
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.:
as in new window
  1. Clifford Hurvich & Eric Moulines & Philippe Soulier, 2004. "Estimating Long Memory in Volatility," Econometrics 0412006, EconWPA.
  2. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  3. Laurent Ferrara & Dominique Guegan, 2008. "Business surveys modelling with seasonal-cyclical long memory models," Documents de travail du Centre d'Economie de la Sorbonne b08035, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  4. Josu Arteche, 2006. "Semiparametric estimation in perturbed long memory series," Computing in Economics and Finance 2006 22, Society for Computational Economics.
  5. Robinson, P. M., 2001. "The memory of stochastic volatility models," Journal of Econometrics, Elsevier, vol. 101(2), pages 195-218, April.
  6. Javier Hidalgo & Philippe Soulier, 2004. "Estimation of the location and exponent of the spectral singularity of a long memory process," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(1), pages 55-81, 01.
  7. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2003. "Forecasting Electricity Demand Using Generalized Long Memory," Economics Working Papers (Ensaios Economicos da EPGE) 486, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
  8. Chung, Ching-Fan, 1996. "Estimating a generalized long memory process," Journal of Econometrics, Elsevier, vol. 73(1), pages 237-259, July.
  9. Luis Alberiko Gil-Alana & Guglielmo M.Caporale, . "Long Memory at the Long Run and at the Cyclical Frequencies:Modelling Real Wages in England: 1260-1994," Faculty Working Papers 18/05, School of Economics and Business Administration, University of Navarra.
  10. J. Arteche & C. Velasco, 2005. "Trimming and Tapering Semi-Parametric Estimates in Asymmetric Long Memory Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(4), pages 581-611, 07.
  11. repec:hal:journl:halshs-00283710 is not listed on IDEAS
  12. Per Frederiksen & Morten Orregaard Nielsen, 2008. "Bias-Reduced Estimation of Long-Memory Stochastic Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(4), pages 496-512, Fall.
  13. repec:hal:journl:halshs-00277379 is not listed on IDEAS
  14. Giraitis, Liudas & Kokoszka, Piotr & Leipus, Remigijus, 2000. "Stationary Arch Models: Dependence Structure And Central Limit Theorem," Econometric Theory, Cambridge University Press, vol. 16(01), pages 3-22, February.
  15. Haldrup, Niels & Nielsen, Morten Oe., . "Estimation of Fractional Integration in the Presence of Data Noise," Economics Working Papers 2003-10, School of Economics and Management, University of Aarhus.
  16. Zaffaroni, Paolo & d'Italia, Banca, 2003. "Gaussian inference on certain long-range dependent volatility models," Journal of Econometrics, Elsevier, vol. 115(2), pages 199-258, August.
  17. Perez, Ana & Ruiz, Esther, 2001. "Finite sample properties of a QML estimator of stochastic volatility models with long memory," Economics Letters, Elsevier, vol. 70(2), pages 157-164, February.
  18. Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
  19. Paolo Zaffaroni, 2003. "Gaussian inference on certain long-range dependent volatility models," Temi di discussione (Economic working papers) 472, Bank of Italy, Economic Research and International Relations Area.
  20. Zaffaroni, Paolo, 2009. "Whittle estimation of EGARCH and other exponential volatility models," Journal of Econometrics, Elsevier, vol. 151(2), pages 190-200, August.
  21. 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.
  22. Arteche, Josu, 2004. "Gaussian semiparametric estimation in long memory in stochastic volatility and signal plus noise models," Journal of Econometrics, Elsevier, vol. 119(1), pages 131-154, March.
  23. Abdou Kâ Diongue & Dominique Guegan, 2004. "Estimating parameters for a k-GIGARCH process," Post-Print halshs-00188531, HAL.
Full references (including those not matched with items on IDEAS)

Citations

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:ehu:biltok:201103. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alcira Macías).

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.