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Comparing emerging and mature markets during times of crises: A non-extensive statistical approach

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  • Namaki, A.
  • Koohi Lai, Z.
  • Jafari, G.R.
  • Raei, R.
  • Tehrani, R.

Abstract

One of the important issues in finance and economics for both scholars and practitioners is to describe the behavior of markets, especially during times of crises. In this paper, we analyze the behavior of some mature and emerging markets with a Tsallis entropy framework that is a non-extensive statistical approach based on non-linear dynamics. During the past decade, this technique has been successfully applied to a considerable number of complex systems such as stock markets in order to describe the non-Gaussian behavior of these systems. In this approach, there is a parameter q, which is a measure of deviation from Gaussianity, that has proved to be a good index for detecting crises. We investigate the behavior of this parameter in different time scales for the market indices. It could be seen that the specified pattern for q differs for mature markets with regard to emerging markets. The findings show the robustness of the stated approach in order to follow the market conditions over time. It is obvious that, in times of crises, q is much greater than in other times. In addition, the response of emerging markets to global events is delayed compared to that of mature markets, and tends to a Gaussian profile on increasing the scale. This approach could be very useful in application to risk and portfolio management in order to detect crises by following the parameter q in different time scales.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:14:p:3039-3044
    DOI: 10.1016/j.physa.2013.02.008
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    Cited by:

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    3. Zhao, Pan & Xiao, Qingxian, 2016. "Portfolio selection problem with liquidity constraints under non-extensive statistical mechanics," Chaos, Solitons & Fractals, Elsevier, vol. 82(C), pages 5-10.
    4. Nikola Gradojevic & Marko Caric, 2017. "Predicting Systemic Risk with Entropic Indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(1), pages 16-25, January.
    5. Gangadhar Nayak & Amit Kumar Singh & Dilip Senapati, 2021. "Computational Modeling of Non-Gaussian Option Price Using Non-extensive Tsallis’ Entropy Framework," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1353-1371, April.
    6. Nikola Gradojevic, 2021. "Brexit and foreign exchange market expectations: Could it have been predicted?," Annals of Operations Research, Springer, vol. 297(1), pages 167-189, February.
    7. Tanmay Mukherjee & Dilip Senapati, 2022. "An adaptive q-Lognormal model towards the computation of average channel capacity in slow fading channels," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 79(3), pages 341-355, March.
    8. Ahmad Hajihasani & Ali Namaki & Nazanin Asadi & Reza Tehrani, 2020. "Non-Extensive Value-at-Risk Estimation During Times of Crisis," Papers 2005.09036, arXiv.org, revised Jan 2021.

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