IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-03c50007.html
   My bibliography  Save this article

Non stationarity characteristics of the S\&P500 returns:An approach based on the evolutionary spectral density

Author

Listed:
  • AHAMADA IBRAHIM

    (GREQAM, université de la méditerranée and CERESUR, université de La Réunion)

Abstract

In this paper we study the characteristics of the non stationarity of the covariance structure of the S\&P 500 returns by analyzing the time spectral density of the data. We show that the S\&P 500 returns has the same characteristics as the modulate white noise process. So, some precautions must be taken before applying traditional stationary models to describe like long size financial time series.

Suggested Citation

  • Ahamada Ibrahim, 2003. "Non stationarity characteristics of the S\&P500 returns:An approach based on the evolutionary spectral density," Economics Bulletin, AccessEcon, vol. 3(32), pages 1-7.
    • Handle: RePEc:ebl:ecbull:eb-03c50007
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/pubs/EB/2003/Volume3/EB-03C50007A.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Loretan, Mico & Phillips, Peter C. B., 1994. "Testing the covariance stationarity of heavy-tailed time series: An overview of the theory with applications to several financial datasets," Journal of Empirical Finance, Elsevier, vol. 1(2), pages 211-248, January.
    2. Ahamada, Ibrahim, 2002. "Tests for covariance stationarity and white noise, with an application to Euro/US dollar exchange rate: An approach based on the evolutionary spectral density," Economics Letters, Elsevier, vol. 77(2), pages 177-186, October.
    3. Pagan, Adrian R. & Schwert, G. William, 1990. "Testing for covariance stationarity in stock market data," Economics Letters, Elsevier, vol. 33(2), pages 165-170, June.
    Full references (including those not matched with items on IDEAS)

    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. Jamel Jouini, 2009. "Analysis of structural break models based on the evolutionary spectrum: Monte Carlo study and application," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(1), pages 91-110.
    2. repec:ebl:ecbull:v:3:y:2003:i:32:p:1-7 is not listed on IDEAS
    3. Leila Nouira & Ibrahim Ahamada & Jamel Jouini & Alain Nurbel, 2004. "Long-memory and shifts in the unconditional variance in the exchange rate euro/US dollar returns," Applied Economics Letters, Taylor & Francis Journals, vol. 11(9), pages 591-594.
    4. Mohamed Boutahar & Jamel Jouini, 2007. "A Methodology For Detecting Breaks In The Mean And Covariance Structure Of Time Series," Working Papers halshs-00354249, HAL.
    5. Chen, Bin & Song, Zhaogang, 2013. "Testing whether the underlying continuous-time process follows a diffusion: An infinitesimal operator-based approach," Journal of Econometrics, Elsevier, vol. 173(1), pages 83-107.
    6. Ibrahim Ahamada & Jamel Jouini & Mohamed Boutahar, 2004. "Detecting multiple breaks in time series covariance structure: a non-parametric approach based on the evolutionary spectral density," Applied Economics, Taylor & Francis Journals, vol. 36(10), pages 1095-1101.
    7. Los, Cornelis A., 1999. "Nonparametric testing of the high-frequency efficiency of the 1997 Asian foreign exchange markets," Journal of Multinational Financial Management, Elsevier, vol. 9(3-4), pages 265-289, November.
    8. Cheng, Xu & Phillips, Peter C.B., 2012. "Cointegrating rank selection in models with time-varying variance," Journal of Econometrics, Elsevier, vol. 169(2), pages 155-165.
    9. O. Beelders, 2003. "An investigation of the unconditional distribution of South African stock index returns," Applied Financial Economics, Taylor & Francis Journals, vol. 13(9), pages 623-633.
    10. Ignacio Mauleon & Javier Perote, 2000. "Testing densities with financial data: an empirical comparison of the Edgeworth-Sargan density to the Student's t," The European Journal of Finance, Taylor & Francis Journals, vol. 6(2), pages 225-239.
    11. West, Kenneth D. & Cho, Dongchul, 1995. "The predictive ability of several models of exchange rate volatility," Journal of Econometrics, Elsevier, vol. 69(2), pages 367-391, October.
    12. Ahamada Ibrahim, 2004. "A complementary test for the KPSS test with an application to the US Dollar/Euro exchange rate," Economics Bulletin, AccessEcon, vol. 3(4), pages 1-5.
    13. Ai Deng & Pierre Perron, 2005. "The Limit Distribution of the CUSUM of Square Test Under Genreal MIxing Conditions," Boston University - Department of Economics - Working Papers Series WP2005-046, Boston University - Department of Economics.
    14. Pedro J. F. de Lima & Michelle L. Barnes, 2000. "Modeling Financial Volatility: Extreme Observations, Nonlinearities and Nonstationarities," School of Economics and Public Policy Working Papers 2000-05, University of Adelaide, School of Economics and Public Policy.
    15. Carmela E. Quintos & Zhenhong Fan & Peter C.B. Phillips, 2000. "Structural Change in Tail Behavior and the Asian Financial Crisis," Cowles Foundation Discussion Papers 1283, Cowles Foundation for Research in Economics, Yale University.
    16. Chen, Bin & Hong, Yongmiao, 2012. "Testing For The Markov Property In Time Series," Econometric Theory, Cambridge University Press, vol. 28(1), pages 130-178, February.
    17. Deng, Ai & Perron, Pierre, 2008. "The Limit Distribution Of The Cusum Of Squares Test Under General Mixing Conditions," Econometric Theory, Cambridge University Press, vol. 24(3), pages 809-822, June.
    18. M. F. Omran, 1997. "Moment condition failure in stock returns: UK evidence," Applied Mathematical Finance, Taylor & Francis Journals, vol. 4(4), pages 201-206.
    19. Mauleon, Ignacio, 2003. "Financial densities in emerging markets: an application of the multivariate ES density," Emerging Markets Review, Elsevier, vol. 4(2), pages 197-223, June.
    20. Lyócsa, Štefan & Baumöhl, Eduard, 2012. "Testing the covariance stationarity of CEE stocks," MPRA Paper 43432, University Library of Munich, Germany.
    21. Zhijie Xiao & Luiz Renato Lima, 2007. "Testing Covariance Stationarity," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 643-667.

    More about this item

    Keywords

    Time-dependent spectral density unconditional volatility S&P 500 returns.;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    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:ebl:ecbull:eb-03c50007. 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: John P. Conley (email available below). General contact details of provider: .

    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.