[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/hin/jnlaaa/143194.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/AAA/2013/143194.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/AAA/2013/143194.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2013/143194?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlaaa:143194. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.