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Long Range Dependence in the Indian Stock Market: Evidence of Fractional Integration, Non-Linearities and Breaks

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  • Luis A. Gil-Alana

    (University of Navarra)

  • Trilochan Tripathy

    () (XLRI-Xavier School of Management)

Abstract

Abstract This paper deals with the analysis of the Indian stock market prices using long range dependence techniques. In particular, we employ a variety of fractionally integrated models, which are very general in the sense that it allows us to incorporate structural breaks and non-linear structures. Our results indicate that the series corresponding to the NSE index is nonstationary and highly persistent, with an order of integration close to or above 1. The volatility, measured in terms of the squared returns indicates that the series is long memory, with an order of integration in the interval (0, 0.5). The results finally support the existence of a mean shift in the data at about January 2008, with the order of integration being around 1. Thus the Efficient Market Hypothesis (EMH) may be satisfied in the Indian stock market once a break is taken into account. However, the existence of short run dynamics suggests a degree of predictability in its behaviour.

Suggested Citation

  • Luis A. Gil-Alana & Trilochan Tripathy, 2016. "Long Range Dependence in the Indian Stock Market: Evidence of Fractional Integration, Non-Linearities and Breaks," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(2), pages 199-215, December.
  • Handle: RePEc:spr:jqecon:v:14:y:2016:i:2:d:10.1007_s40953-016-0029-4
    DOI: 10.1007/s40953-016-0029-4
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    References listed on IDEAS

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    1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    2. John Cotter, 2005. "Uncovering long memory in high frequency UK futures," The European Journal of Finance, Taylor & Francis Journals, vol. 11(4), pages 325-337.
    3. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 261-268, July.
    4. John Y. Campbell & Pierre Perron, 1991. "Pitfalls and Opportunities: What Macroeconomists Should Know About Unit Roots," NBER Chapters,in: NBER Macroeconomics Annual 1991, Volume 6, pages 141-220 National Bureau of Economic Research, Inc.
    5. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    6. Nelson, Charles R & Piger, Jeremy & Zivot, Eric, 2001. "Markov Regime Switching and Unit-Root Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 404-415, October.
    7. Diebold, Francis X. & Rudebusch, Glenn D., 1991. "On the power of Dickey-Fuller tests against fractional alternatives," Economics Letters, Elsevier, vol. 35(2), pages 155-160, February.
    8. Jussi Tolvi, 2003. "Long memory and outliers in stock market returns," Applied Financial Economics, Taylor & Francis Journals, vol. 13(7), pages 495-502.
    9. Granger, Clive W. J. & Hyung, Namwon, 2004. "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 399-421, June.
    10. Gil-Alana, L.A., 2006. "Fractional integration in daily stock market indexes," Review of Financial Economics, Elsevier, vol. 15(1), pages 28-48.
    11. Richard T. Baillie & Young‐Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long memory models for daily and high frequency commodity futures returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(7), pages 643-668, July.
    12. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    13. Crato, Nuno & de Lima, Pedro J. F., 1994. "Long-range dependence in the conditional variance of stock returns," Economics Letters, Elsevier, vol. 45(3), pages 281-285.
    14. DeJong, David N, et al, 1992. "Integration versus Trend Stationarity in Time Series," Econometrica, Econometric Society, vol. 60(2), pages 423-433, March.
    15. Luis A. Gil‐Alana, 2008. "Fractional integration and structural breaks at unknown periods of time," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(1), pages 163-185, January.
    16. Richard T. Baillie & Young-Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long Memory and FIGARCH Models for Daily and High Frequency Commodity Prices," Working Papers 594, Queen Mary University of London, School of Economics and Finance.
    17. 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.
    18. Barkoulas, John T. & Baum, Christopher F., 1996. "Long-term dependence in stock returns," Economics Letters, Elsevier, vol. 53(3), pages 253-259, December.
    19. Summers, Lawrence H, 1986. " Does the Stock Market Rationally Reflect Fundamental Values?," Journal of Finance, American Finance Association, vol. 41(3), pages 591-601, July.
    20. Sadique, Shibley & Silvapulle, Param, 2001. "Long-Term Memory in Stock Market Returns: International Evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(1), pages 59-67, January.
    21. 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.
    22. Lee, Dongin & Schmidt, Peter, 1996. "On the power of the KPSS test of stationarity against fractionally-integrated alternatives," Journal of Econometrics, Elsevier, vol. 73(1), pages 285-302, July.
    23. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    24. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 280-283, July.
    25. Luis A. Gil-Alana & S. G. Brian Henry, 2003. "Fractional Integration and the Dynamics of UK Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(2), pages 221-239, May.
    26. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    27. John Barkoulas & Christopher Baum & Nickolaos Travlos, 2000. "Long memory in the Greek stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 10(2), pages 177-184.
    28. 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.
    29. Olan Henry, 2002. "Long memory in stock returns: some international evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 12(10), pages 725-729.
    30. 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.
    31. Hassler, Uwe & Wolters, Jurgen, 1994. "On the power of unit root tests against fractional alternatives," Economics Letters, Elsevier, vol. 45(1), pages 1-5, May.
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    More about this item

    Keywords

    Stock market; Efficiency; Long memory; India;

    JEL classification:

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

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