IDEAS home Printed from https://ideas.repec.org/a/cuf/journl/y2012v13i1n2.html
   My bibliography  Save this article

The Relationship between Volatility and Trading Volume in the Chinese Stock Market: A Volatility Decomposition Perspective

Author

Listed:
  • Tianyi Wang

    (China Center for Economic Research, National School of Development, Peking University)

  • Zhuo Huang

    (China Center for Economic Research, National School of Development, Peking University)

Abstract

We use heterogeneous autoregressive (HAR) model with high-frequency data of Hu-Shen 300 index to investigate the volatility-volume relationship via the volatility decomposition approach. Although we find that the continuous component of daily volatility is positively correlated with trading volume, the jump component reveals a significant and robust negative relation with volume. This result suggests that the jump component contains some "public information" while the continuous components are more likely driven by "private information". Discussion of the intertemporal relationship supports the information-driven trading hypothesis. Lagged realized skewness only significantly affects the continuous component.

Suggested Citation

  • Tianyi Wang & Zhuo Huang, 2012. "The Relationship between Volatility and Trading Volume in the Chinese Stock Market: A Volatility Decomposition Perspective," Annals of Economics and Finance, Society for AEF, vol. 13(1), pages 211-236, May.
    • Handle: RePEc:cuf:journl:y:2012:v:13:i:1:n:2
    as

    Download full text from publisher

    File URL: http://aeconf.com/Articles/May2012/aef130107.pdf
    Download Restriction: no
    File URL: http://down.aefweb.net/AefArticles/aef130107Wang.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Senarathne, Chamil W & Jayasinghe, Prabhath, 2017. "Information Flow Interpretation of Heteroskedasticity for Capital Asset Pricing: An Expectation-based View of Risk," MPRA Paper 78771, University Library of Munich, Germany, revised 04 Apr 2017.
    2. Rudolf Plachý, 2014. "Impact of Trading Volume on Prediction of Stock Market Development," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 62(6), pages 1373-1380.
    3. Saswat Patra & Malay Bhattacharyya, 2021. "Does volume really matter? A risk management perspective using cross‐country evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 118-135, January.
    4. Slim, Skander & Dahmene, Meriam, 2016. "Asymmetric information, volatility components and the volume–volatility relationship for the CAC40 stocks," Global Finance Journal, Elsevier, vol. 29(C), pages 70-84.
    5. Hang Zhang & Evangelos Giouvris, 2022. "Measures of Volatility, Crises, Sentiment and the Role of U.S. ‘Fear’ Index (VIX) on Herding in BRICS (2007–2021)," JRFM, MDPI, vol. 15(3), pages 1-42, March.
    6. Jiqian Wang & Feng Ma & Chao Liang & Zhonglu Chen, 2022. "Volatility forecasting revisited using Markov‐switching with time‐varying probability transition," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1387-1400, January.
    7. Wang, Yajing & Liang, Fang & Wang, Tianyi & Huang, Zhuo, 2020. "Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market," Economic Modelling, Elsevier, vol. 87(C), pages 148-157.
    8. Cheng Jiang & Kose John & David Larsen, 2021. "R&D investment intensity and jump volatility of stock price," Review of Quantitative Finance and Accounting, Springer, vol. 57(1), pages 235-277, July.
    9. Min Liu & Wei‐Chong Choo & Chi‐Chuan Lee & Chien‐Chiang Lee, 2023. "Trading volume and realized volatility forecasting: Evidence from the China stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 76-100, January.
    10. Dimitrios I. Vortelinos, 2015. "The Effect of Macro News on Volatility and Jumps," Annals of Economics and Finance, Society for AEF, vol. 16(2), pages 425-447, November.
    11. Karaa, Rabaa & Slim, Skander & Hmaied, Dorra Mezzez, 2018. "Trading intensity and the volume-volatility relationship on the Tunis Stock Exchange," Research in International Business and Finance, Elsevier, vol. 44(C), pages 88-99.
    12. Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo, 2021. "An empirical study on the role of trading volume and data frequency in volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 792-816, August.
    13. Tosin B. Fateye & Oluwaseun D. Ajay & Cyril A. Ajay, 2021. "Modelling of Daily Price Volatility of South Africa Property Stock Market Using GARCH Analysis," AfRES 2021-013, African Real Estate Society (AfRES).
    14. Beata Szetela & Grzegorz Mentel & Yuriy Bilan & Urszula Mentel, 2021. "The relationship between trend and volume on the bitcoin market," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(1), pages 25-42, March.
    15. Jawadi Fredj & Ureche-Rangau Loredana, 2013. "Threshold linkages between volatility and trading volume: evidence from developed and emerging markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(3), pages 313-333, May.
    16. Nathan Mwenda Mutwiri & Job Omagwa & Lucy Wamugo, 2021. "Systematic risk and performance of stock market in Kenya," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 10(4), pages 204-214, June.

    More about this item

    Keywords

    High frequency; Price jump; Trading volume;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    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:cuf:journl:y:2012:v:13:i:1:n:2. 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: Qiang Gao (email available below). General contact details of provider: https://edirc.repec.org/data/emcufcn.html .

    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.