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Modeling the distribution of extreme returns in the Chinese stock market

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  • Hussain, Saiful Izzuan
  • Li, Steven

Abstract

It is well known that extreme share returns on stock markets can have important implications for financial risk management. In this paper, we are concerned with the distribution of the extreme daily returns of the Shanghai Stock Exchange (SSE) Composite Index. Three well-known distributions in extreme value theory, i.e., Generalized Extreme Value (GEV), Generalized Logistic (GL) and Generalized Pareto distributions, are employed to model the SSE Composite index returns based on the data from 1991 to 2013. The parameters for each distribution are estimated by using the Power Weighted Method (PWM). Our results indicate that the GL distribution is a better fit for the minima series and that the GEV distribution is a better fit for the maxima series of the returns for the Chinese stock market. This is in contrast to the findings for other markets, such as the US and Singapore markets. Our results are robust regardless of the introduction of stock movement restriction and the global financial crisis. Further, the implications of our findings for risk management are discussed.

Suggested Citation

  • Hussain, Saiful Izzuan & Li, Steven, 2015. "Modeling the distribution of extreme returns in the Chinese stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 34(C), pages 263-276.
    • Handle: RePEc:eee:intfin:v:34:y:2015:i:c:p:263-276
    DOI: 10.1016/j.intfin.2014.11.007
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    Cited by:

    1. Saiful Izzuan Hussain & Steven Li, 2018. "The dynamic dependence between stock markets in the greater China economic area: a study based on extreme values and copulas," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(2), pages 207-233, May.
    2. Bian, Jiangze & Chan, Kalok & Han, Bing & Shi, Donghui, 2023. "Cross-border equity flows and information transmission: Evidence from Chinese stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
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    4. Han, Chenyu & Wang, Yiming & Ning, Ye, 2019. "Analysis and comparison of the multifractality and efficiency of Chinese stock market: Evidence from dynamics of major indexes in different boards," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C), pages 1-1.
    5. Singh, Vikkram & Roca, Eduardo & Li, Bin, 2021. "Effectiveness of policy interventions during financial crises in China and Russia: Lessons for the COVID-19 pandemic," Journal of Policy Modeling, Elsevier, vol. 43(2), pages 253-277.
    6. Si, Deng-Kui & Liu, Xi-Hua & Kong, Xianli, 2019. "The comovement and causality between stock market cycle and business cycle in China: Evidence from a wavelet analysis," Economic Modelling, Elsevier, vol. 83(C), pages 17-30.
    7. Viviane Naimy & José-María Montero & Rim El Khoury & Nisrine Maalouf, 2020. "Market Volatility of the Three Most Powerful Military Countries during Their Intervention in the Syrian War," Mathematics, MDPI, vol. 8(5), pages 1-21, May.
    8. Abdul-Aziz Ibn Musah & Jianguo Du & Hira Salah Ud din Khan & Alhassan Alolo Abdul-Rasheed Akeji, 2018. "The Asymptotic Decision Scenarios of an Emerging Stock Exchange Market: Extreme Value Theory and Artificial Neural Network," Risks, MDPI, vol. 6(4), pages 1-24, November.

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