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Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model

Author

Listed:
  • Chuangxia Huang
  • Xu Gong
  • Xiaohong Chen
  • Fenghua Wen

Abstract

Basing on the Heterogeneous Autoregressive with Continuous volatility and Jumps model (HAR-CJ), converting the realized Volatility (RV) into the adjusted realized volatility (ARV), and making use of the influence of momentum effect on the volatility, a new model called HAR-CJ-M is developed in this paper. At the same time, we also address, in great detail, another two models (HAR-ARV, HAR-CJ). The applications of these models to Chinese stock market show that each of the continuous sample path variation, momentum effect, and ARV has a good forecasting performance on the future ARV, while the discontinuous jump variation has a poor forecasting performance. Moreover, the HAR-CJ-M model shows obviously better forecasting performance than the other two models in forecasting the future volatility in Chinese stock market.

Suggested Citation

  • Chuangxia Huang & Xu Gong & Xiaohong Chen & Fenghua Wen, 2013. "Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-13, March.
    • Handle: RePEc:hin:jnlaaa:143194
    DOI: 10.1155/2013/143194
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    Cited by:

    1. Gong, Xu & Lin, Boqiang, 2018. "The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market," Energy Economics, Elsevier, vol. 74(C), pages 370-386.
    2. Lai, Lin & Guo, Kun, 2017. "The performance of one belt and one road exchange rate: Based on improved singular spectrum analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 299-308.
    3. Xuemei Gao & Dongya Deng & Yue Shan, 2014. "Lattice Methods for Pricing American Strangles with Two-Dimensional Stochastic Volatility Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-6, April.
    4. Tapia, Sebastian & Kristjanpoller, Werner, 2022. "Framework based on multiplicative error and residual analysis to forecast bitcoin intraday-volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    5. Hassanniakalager, Arman & Baker, Paul L. & Platanakis, Emmanouil, 2024. "A False Discovery Rate approach to optimal volatility forecasting model selection," International Journal of Forecasting, Elsevier, vol. 40(3), pages 881-902.
    6. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
    7. Gong, Xu & Lin, Boqiang, 2019. "Modeling stock market volatility using new HAR-type models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 194-211.
    8. Xiao, Jihong & Wen, Fenghua & Zhao, Yupei & Wang, Xiong, 2021. "The role of US implied volatility index in forecasting Chinese stock market volatility: Evidence from HAR models," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 311-333.
    9. Dongmei Guo & Yi Hu & Bingjie Zhang, 2014. "Carbon Market Regulation Mechanism Research Based on Carbon Accumulation Model with Jump Diffusion," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-7, May.
    10. Gang-Jin Wang & Chi Xie & Peng Zhang & Feng Han & Shou Chen, 2014. "Dynamics of Foreign Exchange Networks: A Time-Varying Copula Approach," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-11, May.
    11. Xu, Liao & Gao, Han & Shi, Yukun & Zhao, Yang, 2020. "The heterogeneous volume-volatility relations in the exchange-traded fund market: Evidence from China," Economic Modelling, Elsevier, vol. 85(C), pages 400-408.
    12. Campos, I. & Cortazar, G. & Reyes, T., 2017. "Modeling and predicting oil VIX: Internet search volume versus traditional mariables," Energy Economics, Elsevier, vol. 66(C), pages 194-204.
    13. Arnerić Josip & Poklepović Tea & Teai Juin Wen, 2018. "Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data," Business Systems Research, Sciendo, vol. 9(2), pages 18-34, July.
    14. Wang, Dan & Huang, Wei-Qiang, 2021. "Centrality-based measures of financial institutions’ systemic importance: A tail dependence network view," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    15. Lili Li & Shan Leng & Jun Yang & Mei Yu, 2016. "Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-15, September.
    16. George S. Atsalakis & Eftychios E. Protopapadakis & Kimon P. Valavanis, 2016. "Stock trend forecasting in turbulent market periods using neuro-fuzzy systems," Operational Research, Springer, vol. 16(2), pages 245-269, July.

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