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A Statistical Analysis of Chinese Stock Indices Returns From Approach of Parametric Distributions Fitting

Author

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  • Yuancheng Si

    (Anhui Agricultural University
    Bank of HuZhou)

  • Saralees Nadarajah

    (University of Manchester)

Abstract

The stock price process in China is full of uncertainty hence the stock indices were introduced to serve as indicators of the financial market. How to capture the statistical characteristics of Chinese stock indices returns by the method of parametric distributions fitting could be useful in the fields of econometrics and risk management. In this paper, we use a wider range of parametric distributions to model four main Chinese stock indices. We find a generalization of the Student’s t distribution is shown to provide the best fit.

Suggested Citation

  • Yuancheng Si & Saralees Nadarajah, 2023. "A Statistical Analysis of Chinese Stock Indices Returns From Approach of Parametric Distributions Fitting," Annals of Data Science, Springer, vol. 10(1), pages 73-88, February.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:1:d:10.1007_s40745-022-00421-9
    DOI: 10.1007/s40745-022-00421-9
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    References listed on IDEAS

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    1. B. Shravan Kumar & Vadlamani Ravi & Rishabh Miglani, 2021. "Predicting Indian Stock Market Using the Psycho-Linguistic Features of Financial News," Annals of Data Science, Springer, vol. 8(3), pages 517-558, September.
    2. Hedayati , Amin & Hedayati , Moein & Esfandyari, Morteza, 2016. "Stock market index prediction using artificial neural network," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 21(41), pages 89-93.
    3. Zhu, Dongming & Zinde-Walsh, Victoria, 2009. "Properties and estimation of asymmetric exponential power distribution," Journal of Econometrics, Elsevier, vol. 148(1), pages 86-99, January.
    4. Zhu, Dongming & Galbraith, John W., 2010. "A generalized asymmetric Student-t distribution with application to financial econometrics," Journal of Econometrics, Elsevier, vol. 157(2), pages 297-305, August.
    5. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    6. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    7. Chung, San-Lin & Hung, Chi-Hsiou & Yeh, Chung-Ying, 2012. "When does investor sentiment predict stock returns?," Journal of Empirical Finance, Elsevier, vol. 19(2), pages 217-240.
    8. McDonald, James B. & Newey, Whitney K., 1988. "Partially Adaptive Estimation of Regression Models via the Generalized T Distribution," Econometric Theory, Cambridge University Press, vol. 4(3), pages 428-457, December.
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