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Stylized Facts of the Indian Stock Market

Author

Listed:
  • Rituparna Sen

    (Indian Statistical Institute)

  • Manavathi Subramaniam

    (PSG College of Technology)

Abstract

Historical daily data for eleven years of the fifty constituent stocks of the NIFTY index traded on the National Stock Exchange have been analyzed to check for the stylized facts in the Indian market. It is observed that while some stylized facts of other markets are also observed in Indian market, there are significant deviations in three main aspects, namely leverage, asymmetry and autocorrelation. Leverage and asymmetry are both reversed making this a more promising market to invest in. While significant autocorrelation observed in the returns points towards market inefficiency, the increased predictive power is better for investors.

Suggested Citation

  • Rituparna Sen & Manavathi Subramaniam, 2019. "Stylized Facts of the Indian Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(4), pages 479-493, December.
    • Handle: RePEc:kap:apfinm:v:26:y:2019:i:4:d:10.1007_s10690-019-09275-3
    DOI: 10.1007/s10690-019-09275-3
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    References listed on IDEAS

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    1. 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.
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    4. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
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    More about this item

    Keywords

    National stock exchange; Volatility clustering; Leverage effect; Heavy tails; Power law;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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