On the Volatility of High Frequency Stock Index Based on SV Model of MCMC

  • Y. X. Zheng
  • Y. H. ZhangEmail author
  • X. H. Lu
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 302)


By using of 5-min high-frequency data in CSI 300 index stock high-frequency data from 15th Jan. 2018 to 5th Mar. 2018, basing on Bayesian Analysis simulated by MCMC, this paper adopts the Stochastic Volatility model to do empirical researches on China’s stock market and utilizes DIC criterion to do model fitting comparison. The result shows that China’s stock market has higher volatility persistence, and the fitting effect for SV model to 5-min high-frequency data is better than the low-frequency data, and the standard stochastic volatility model (SV-N) is more suitable for high frequency-data of 5-min than the heavy-tail finance stochastic volatility model (SV-T).


SV model Gibbs sampling Bayesian analysis Monte Carlo method 



The work was partly supported by National Natural Science Foundation of China (Grant No. 11971042) and National Science Foundation of Beijing Municipality (Grant No. 1182008).


  1. 1.
    Andersen, T., Bollerslev, T.: (Super) High frequency data analysis and modeling. Stat Study (11), 28–31 (2002)Google Scholar
  2. 2.
    Andersen, T., Bollerslev, T.: The distribution of exchange rate volatility. J. Am. Stat. Assoc. 96(457), 42–55 (2000)Google Scholar
  3. 3.
    Andersen, T., Bollerslev, T.: Answering the skeptics: yes, Standard volatility models do provide accurate forecasts. Int. Econ. Rev. 39(4), 885–905 (1998)CrossRefGoogle Scholar
  4. 4.
    Gao, T.M.: Method and Modeling of Econometric Analysis, vol. 14. Tsinghua University Press (2009)Google Scholar
  5. 5.
    Li, S.G., Zhang, S.Y.: Financial volatility model based on high frequency data. Stat Decis (1), 7–8 (2008)Google Scholar
  6. 6.
    Li, G.H.: Study on fluctuation of coastal dry bulk freight rate based on stochastic volatility model. Res. Dev. Sci. Technol. World 38(3) (2016)Google Scholar
  7. 7.
    Liu, F.Q.: Comparison of SV model based on DIC criterion. Stat. Decis. (9) (2004)Google Scholar
  8. 8.
    Lu, X.H., Zhang, Y.H., Zheng, Y.X.: Research and comparison of wavelet estimation methods for high frequency data volatility. Stat. Decis. (2018)Google Scholar
  9. 9.
    Madhu K., Raul S.: Regime-switching stochastic volatility and short-term interest rates. J. Empir Financ. 11(3), 309–329 (2004)Google Scholar
  10. 10.
    Meyer, R., Yu, J.: BUGS for a Bayesian analysis of stochastic volatility models. Econ. J. (S1368-4221) 3, 198–215 (2000)CrossRefGoogle Scholar
  11. 11.
    Robert, C.P., Casella, G.: Monte Carlo Statistical Mothods. Springer, New York (2004)CrossRefGoogle Scholar
  12. 12.
    Spiegelhalter, D.J., Best, N.G., Carlin, B.P.: Bayesian measures of model complexity and fit (with Discussion). J. R. Stat. Soc. Ser. B 64(4), 583–616 (2002)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Sun, Z.H.: An analysis of the pulsating characteristics of the gold market in China. J. Changsha Univer. Sci. Technol. 30(2) (2015)Google Scholar
  14. 14.
    Wang, T.Y., Huang, Z.: Research on modeling and application of volatility based on high frequency data. Econ. Perspect (3), 141–146 (2012)Google Scholar
  15. 15.
    Wang, C.F., Zhuang, H.G., Fang, Z.M., Lu, T.: Estimation and volatility prediction of long memory stochastic volatility models research on high frequency data of China’s stock market. Syst. Eng. 7(26), 29–34 (2008)Google Scholar
  16. 16.
    Yang, J.Y., Zhang, Y.H.: Research on the market liquidity of stock index futures based on ACD model. Math. Prac. Theory 46(9), 54–60 (2016)Google Scholar
  17. 17.
    Yang, J.Y., Zhang, Y.H.: Impact of circuit-breaker mechanism on chinas a share market. Stat. Decis. 13, 153–155 (2017)Google Scholar
  18. 18.
    Zhang, B., Yu, C., Bi, T.: High Frequency Financial Data Modeling Theory, Method and Application, vol. 1, pp. 3–6. Tsinghua University Press (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Technology Support GroupMigu Culture Technology Com., LtdBeijingChina
  2. 2.Department of MathematicsBeijing Technology and Business UniversityBeijingChina
  3. 3.School of Statistics and MathematicsCentral University of Finance and EconomicsBeijingChina

Personalised recommendations