IDEAS home Printed from https://ideas.repec.org/a/taf/apfiec/v8y1998i4p435-443.html
   My bibliography  Save this article

Short and long-run dependence in Swedish stock returns

Author

Listed:
  • Lennart Berg
  • Johan Lyhagen

Abstract

The behaviour of Swedish stock returns over short and long-run horizons is analysed. Using monthly data from 1919 to 1995 and, weekly and daily data for the 1980s and first part of the 1990s little evidence of long-run dependence was found. Using three different tests that are robust to short-term dependence and conditional hetroscedasticity it was found that the modified R/S (rescaled range) test and ARFIMA-GARCH tests provided no support for long-run memory in Swedish stock returns. Only the fractional differencing test, GPH, gave a significant result in two cases: for monthly real and nominal stock returns for the full and the first half of the sample at rather high frequency for the spectral analysis.

Suggested Citation

  • Lennart Berg & Johan Lyhagen, 1998. "Short and long-run dependence in Swedish stock returns," Applied Financial Economics, Taylor & Francis Journals, vol. 8(4), pages 435-443.
    • Handle: RePEc:taf:apfiec:v:8:y:1998:i:4:p:435-443
    DOI: 10.1080/096031098332961
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/096031098332961
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/096031098332961?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    2. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    3. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    4. Frennberg, Per & Hansson, Bjorn, 1993. "Testing the random walk hypothesis on Swedish stock prices: 1919-1990," Journal of Banking & Finance, Elsevier, vol. 17(1), pages 175-191, February.
    5. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    6. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
    7. Mandelbrot, Benoit B, 1971. "When Can Price Be Arbitraged Efficiently? A Limit to the Validity of the Random Walk and Martingale Models," The Review of Economics and Statistics, MIT Press, vol. 53(3), pages 225-236, August.
    8. Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb..
    9. Cheung, Yin-Wong & Lai, Kon S., 1995. "A search for long memory in international stock market returns," Journal of International Money and Finance, Elsevier, vol. 14(4), pages 597-615, August.
    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. Andreas Graflund, 2000. "A Bayes Inference Approach to Testing Mean Reversion in the Swedish Stock Market," Econometric Society World Congress 2000 Contributed Papers 1363, Econometric Society.
    2. Djeutcha, Eric & Kamdem, Jules Sadefo, 2021. "Local and implied volatilities with the mixed-modified-fractional-Dupire model," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    3. Graflund, Andreas, 2000. "A Bayesian Inference Approach to Testing Mean Reversion in the Swedish Stock Market," Working Papers 2000:8, Lund University, Department of Economics, revised 30 Jan 2002.
    4. Mansour Zarra-Nezhad & Ali Raoofi & Mohammad Hadi Akbarzdeh, 2016. "The Existence of Long Memory Property in OPEC Oil Prices," Asian Journal of Economic Modelling, Asian Economic and Social Society, vol. 4(3), pages 142-152, September.
    5. Guglielmo Maria Caporale & Luis A. Gil-Alana & Alex Plastun, 2017. "Long Memory and Data Frequency in Financial Markets," Discussion Papers of DIW Berlin 1647, DIW Berlin, German Institute for Economic Research.
    6. Mynhardt, H. R. & Plastun, Alex & Makarenko, Inna, 2014. "Behavior of Financial Markets Efficiency During the Financial Market Crisis: 2007-2009," MPRA Paper 58942, University Library of Munich, Germany.
    7. Lennart Berg, 2003. "Deterministic Seasonal Volatility in a Small and Integrated Stock Market: The Case of Sweden," Finnish Economic Papers, Finnish Economic Association, vol. 16(2), pages 61-71, Autumn.
    8. A. M. M. Shahiduzzaman Quoreshi & Reaz Uddin & Viroj Jienwatcharamongkhol, 2019. "Equity Market Contagion in Return Volatility during Euro Zone and Global Financial Crises: Evidence from FIMACH Model," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    9. Sun, Lin, 2013. "Pricing currency options in the mixed fractional Brownian motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3441-3458.
    10. Tan, Pei P. & Galagedera, Don U.A. & Maharaj, Elizabeth A., 2012. "A wavelet based investigation of long memory in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2330-2341.
    11. Luis Gil-Alana & Pedro Mendi, 2005. "Fractional integration in total factor productivity: evidence from US data," Applied Economics, Taylor & Francis Journals, vol. 37(12), pages 1369-1383.
    12. Bhandari, Avishek, 2020. "Long memory and fractality among global equity markets: A multivariate wavelet approach," MPRA Paper 99653, University Library of Munich, Germany.
    13. Xiao, Wei-Lin & Zhang, Wei-Guo & Zhang, Xi-Li & Wang, Ying-Luo, 2010. "Pricing currency options in a fractional Brownian motion with jumps," Economic Modelling, Elsevier, vol. 27(5), pages 935-942, September.
    14. Ahmadian, D. & Ballestra, L.V. & Shokrollahi, F., 2022. "A Monte-Carlo approach for pricing arithmetic Asian rainbow options under the mixed fractional Brownian motion," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    15. Kristoufek, Ladislav, 2009. "Distinguishing between short and long range dependence: Finite sample properties of rescaled range and modified rescaled range," MPRA Paper 16424, University Library of Munich, Germany.

    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. Gil-Alana, L.A., 2006. "Fractional integration in daily stock market indexes," Review of Financial Economics, Elsevier, vol. 15(1), pages 28-48.
    2. Cheung, Yin-Wong & Lai, Kon S., 1995. "A search for long memory in international stock market returns," Journal of International Money and Finance, Elsevier, vol. 14(4), pages 597-615, August.
    3. Onali, Enrico & Goddard, John, 2009. "Unifractality and multifractality in the Italian stock market," International Review of Financial Analysis, Elsevier, vol. 18(4), pages 154-163, September.
    4. Gomes, Luís M. P. & Soares, Vasco J. S. & Gama, Sílvio M. A. & Matos, José A. O., 2018. "Long-term memory in Euronext stock indexes returns: an econophysics approach," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(4), pages 862-881, August.
    5. Ngene, Geoffrey & Tah, Kenneth A. & Darrat, Ali F., 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, Elsevier, vol. 34(C), pages 61-73.
    6. Gerlich, Nikolas & Rostek, Stefan, 2015. "Estimating serial correlation and self-similarity in financial time series—A diversification approach with applications to high frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 84-98.
    7. Doyle, John R. & Chen, Catherine Huirong, 2012. "A multidimensional classification of market anomalies: Evidence from 76 price indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(5), pages 1237-1257.
    8. Hull, Matthew & McGroarty, Frank, 2014. "Do emerging markets become more efficient as they develop? Long memory persistence in equity indices," Emerging Markets Review, Elsevier, vol. 18(C), pages 45-61.
    9. F. DePenya & L. Gil-Alana, 2006. "Testing of nonstationary cycles in financial time series data," Review of Quantitative Finance and Accounting, Springer, vol. 27(1), pages 47-65, August.
    10. Guglielmo Maria Caporale & Luis Gil-Alana, 2011. "The weekly structure of US stock prices," Applied Financial Economics, Taylor & Francis Journals, vol. 21(23), pages 1757-1764.
    11. Goddard, John & Onali, Enrico, 2012. "Self-affinity in financial asset returns," International Review of Financial Analysis, Elsevier, vol. 24(C), pages 1-11.
    12. Boutahar, Mohamed & Mootamri, Imène & Péguin-Feissolle, Anne, 2009. "A fractionally integrated exponential STAR model applied to the US real effective exchange rate," Economic Modelling, Elsevier, vol. 26(2), pages 335-341, March.
    13. Cajueiro, Daniel O. & Tabak, Benjamin M., 2006. "Testing for predictability in equity returns for European transition markets," Economic Systems, Elsevier, vol. 30(1), pages 56-78, March.
    14. Mouck, T., 1998. "Capital markets research and real world complexity: The emerging challenge of chaos theory," Accounting, Organizations and Society, Elsevier, vol. 23(2), pages 189-203, February.
    15. Saadet Kasman & Evrim Turgutlu & A. Duygu Ayhan, 2009. "Long memory in stock returns: evidence from the major emerging Central European stock markets," Applied Economics Letters, Taylor & Francis Journals, vol. 16(17), pages 1763-1768.
    16. Erhard Reschenhofer & Manveer K. Mangat, 2020. "Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data," Econometrics, MDPI, vol. 8(4), pages 1-15, October.
    17. Gil-Alana, Luis A. & Cunado, Juncal & de Gracia, Fernando Perez, 2013. "Salient features of dependence in daily US stock market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(15), pages 3198-3212.
    18. Hiemstra, Craig & Jones, Jonathan D., 1997. "Another look at long memory in common stock returns," Journal of Empirical Finance, Elsevier, vol. 4(4), pages 373-401, December.
    19. Massimiliano Giacalone & Demetrio Panarello, 2022. "A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments," Mathematics, MDPI, vol. 10(5), pages 1-21, February.
    20. Erhard Reschenhofer & Manveer K. Mangat, 2021. "Fast computation and practical use of amplitudes at non-Fourier frequencies," Computational Statistics, Springer, vol. 36(3), pages 1755-1773, September.

    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:apfiec:v:8:y:1998:i:4:p:435-443. 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/RAFE20 .

    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.