IDEAS home Printed from https://ideas.repec.org/a/jfr/afr111/v4y2015i2p50.html
   My bibliography  Save this article

A Simultaneous Equations Model of Returns, Volatility, And Volume With Intraday Trading Dynamics

Author

Listed:
  • Megan Y. Sun
  • June F. Li

Abstract

This paperextends the finance literature by modeling stock returns, volatility, andvolume in a simultaneous equations model while incorporating the effects oftrading dynamics on these three variables. Evidence shows that returns, volatility, and volume are interrelated. However, research typically examined them asthree separate relationships between each pair of the three variables. Priorliterature has also failed to examine the impact of intraday trading dynamicson returns, volatility, and volume. Thisstudy overcomes both limitations. Usinga simultaneous equations model that incorporates feedbacks among these threevariables, this study documents that intraday skewness hassignificant impacts on daily returns, volatility, and volume. In addition, the two-way relationshipsbetween the variables change significantly when they are estimatedsimultaneously. The findings in thisstudy deepen our understanding of the relationships between returns,volatility, and volume and have important implications for traders, portfoliomanagers, and other market participants.

Suggested Citation

  • Megan Y. Sun & June F. Li, 2015. "A Simultaneous Equations Model of Returns, Volatility, And Volume With Intraday Trading Dynamics," Accounting and Finance Research, Sciedu Press, vol. 4(2), pages 1-50, May.
    • Handle: RePEc:jfr:afr111:v:4:y:2015:i:2:p:50
    as

    Download full text from publisher

    File URL: https://www.sciedupress.com/journal/index.php/afr/article/download/6524/3959
    Download Restriction: no
    File URL: https://www.sciedupress.com/journal/index.php/afr/article/view/6524
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wood, Robert A & McInish, Thomas H & Ord, J Keith, 1985. "An Investigation of Transactions Data for NYSE Stocks," Journal of Finance, American Finance Association, vol. 40(3), pages 723-739, July.
    2. Liesenfeld, Roman, 1998. "Dynamic Bivariate Mixture Models: Modeling the Behavior of Prices and Trading Volume," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 101-109, January.
    3. George H. K. Wang & Jot Yau, 2000. "Trading volume, bid–ask spread, and price volatility in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(10), pages 943-970, November.
    4. Liesenfeld, Roman, 2001. "A generalized bivariate mixture model for stock price volatility and trading volume," Journal of Econometrics, Elsevier, vol. 104(1), pages 141-178, August.
    5. Bange, Mary M., 2000. "Do the Portfolios of Small Investors Reflect Positive Feedback Trading?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(2), pages 239-255, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anthony Murphy & Marwan Izzeldin, 2005. "Order Flow, Transaction Clock, and Normality of Asset Returns: A Comment on Ané and Geman (2000)," Finance 0512005, University Library of Munich, Germany.
    2. repec:lan:wpaper:3054 is not listed on IDEAS
    3. Hautsch, Nikolaus, 2008. "Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(12), pages 3978-4015, December.
    4. repec:lan:wpaper:3052 is not listed on IDEAS
    5. 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.
    6. Kusen, Alex & Rudolf, Markus, 2019. "Feedback trading: Strategies during day and night with global interconnectedness," Research in International Business and Finance, Elsevier, vol. 48(C), pages 438-463.
    7. repec:lan:wpaper:3326 is not listed on IDEAS
    8. repec:lan:wpaper:3050 is not listed on IDEAS
    9. repec:lan:wpaper:3048 is not listed on IDEAS
    10. Marwan Izzeldin, 2007. "Trading volume and the number of trades," Working Papers 584864, Lancaster University Management School, Economics Department.
    11. Katarzyna Maciejowska, 2010. "Estimation Methods Comparison of SVAR Models with a Mixture of Two Normal Distributions," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 2(4), pages 279-314, September.
    12. Loredana Ureche-Rangau & Fabien Collado & Ulysse Galiay, 2011. "The dynamics of the volatility – trading volume relationship: New evidence from developed and emerging markets," Economics Bulletin, AccessEcon, vol. 31(3), pages 2569-2583.
    13. Liesenfeld, Roman & Richard, Jean-Francois, 2003. "Univariate and multivariate stochastic volatility models: estimation and diagnostics," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 505-531, September.
    14. Ané, Thierry & Ureche-Rangau, Loredana, 2008. "Does trading volume really explain stock returns volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 216-235, July.
    15. M. Hakan Eratalay, 2016. "Estimation of Multivariate Stochastic Volatility Models: A Comparative Monte Carlo Study," International Econometric Review (IER), Econometric Research Association, vol. 8(2), pages 19-52, September.
    16. Sarika Mahajan & Balwinder Singh, 2013. "Return, Volume and Volatility Relationship in Indian Stock Market: Pre and Post Rolling Settlement Analysis," Global Business Review, International Management Institute, vol. 14(3), pages 413-428, September.
    17. Su, Fei & Wang, Xinyi & Yuan, Yulin, 2022. "The intraday dynamics and intraday price discovery of bitcoin," Research in International Business and Finance, Elsevier, vol. 60(C).
    18. repec:lan:wpaper:3142 is not listed on IDEAS
    19. Carmen Broto & Esther Ruiz, 2004. "Estimation methods for stochastic volatility models: a survey," Journal of Economic Surveys, Wiley Blackwell, vol. 18(5), pages 613-649, December.
    20. Maciejowska, Katarzyna, 2013. "Assessing the number of components in a normal mixture: an alternative approach," MPRA Paper 50303, University Library of Munich, Germany.
    21. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    22. Vasyl Golosnoy & Yarema Okhrin, 2015. "Using information quality for volatility model combinations," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 1055-1073, June.
    23. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    24. Asai, Manabu & Brugal, Ivan, 2013. "Forecasting volatility via stock return, range, trading volume and spillover effects: The case of Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 202-213.
    25. Wang, Jianxin, 2022. "Market distraction and near-zero daily volatility persistence," International Review of Financial Analysis, Elsevier, vol. 80(C).
    26. Anthony Murphy & Marwan Izzeldin, 2010. "Recovering the moments of information flow and the normality of asset returns," Applied Financial Economics, Taylor & Francis Journals, vol. 20(10), pages 761-769.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    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:jfr:afr111:v:4:y:2015:i:2:p:50. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sciedu Press (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.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.