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Evaluating volatile stock markets using information theoretic measures


  • Batra, Luckshay
  • Taneja, H.C.


In this paper, we broaden the entropy concept for Indian financial markets to make a collation between markets which are highly volatile. We have considered seven different estimators of Shannon entropy; Tsallis entropy and Renyi entropy for various values of their parameters; and also Approximate entropy and Sample entropy. For all these entropies, we provide computational results for minute-wise and day-wise data in terms of NIFTY stock and NIFTY sector indices. We conclude that in context of sector wise indices and stock wise indices, NIFTY Pharma and NIFTY50 are the most volatile among other indices respectively.

Suggested Citation

  • Batra, Luckshay & Taneja, H.C., 2020. "Evaluating volatile stock markets using information theoretic measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
  • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315444
    DOI: 10.1016/j.physa.2019.122711

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    References listed on IDEAS

    1. Saumitra N. Bhaduri, 2014. "Applying Approximate Entropy (ApEn) to Speculative Bubble in the Stock Market," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 13(1), pages 43-68, April.
    2. Imen Mahmoud & Kamel Naoui & Hatem Jemmali, 2013. "Study of Speculative Bubbles: The Contribution of Approximate Entropy," International Journal of Economics and Financial Issues, Econjournals, vol. 3(3), pages 683-693.
    3. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    4. Petr Jizba & Hagen Kleinert & Mohammad Shefaat, 2011. "Renyi's information transfer between financial time series," Papers 1106.5913,, revised Jan 2012.
    5. Jizba, Petr & Kleinert, Hagen & Shefaat, Mohammad, 2012. "Rényi’s information transfer between financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(10), pages 2971-2989.
    6. Xia, Jianan & Shang, Pengjian & Wang, Jing & Shi, Wenbin, 2014. "Classifying of financial time series based on multiscale entropy and multiscale time irreversibility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 151-158.
    7. Huang, Jingjing & Shang, Pengjian & Zhao, Xiaojun, 2012. "Multifractal diffusion entropy analysis on stock volatility in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5739-5745.
    8. Namaki, A. & Koohi Lai, Z. & Jafari, G.R. & Raei, R. & Tehrani, R., 2013. "Comparing emerging and mature markets during times of crises: A non-extensive statistical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(14), pages 3039-3044.
    9. Eom, Cheoljun & Oh, Gabjin & Jung, Woo-Sung, 2008. "Relationship between efficiency and predictability in stock price change," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5511-5517.
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