IDEAS home Printed from https://ideas.repec.org/p/unp/wpaper/200910.html
   My bibliography  Save this paper

Liquidity Measurement Based on Bid-Ask Spread, Trading Frequency, and Liquidity Ratio: The Use of GARCH Model on Jakarta Stock Exchange (JSX)

Author

Listed:
  • Erie Febrian

    (Finance & Risk Management Study Group (FRMSG) FE UNPAD)

  • Aldrin Herwany

    (Research Division, Laboratory of Management FE UNPAD)

Abstract

This paper attempts to investigate and clarify previous studies on market liquidity measurement, which involve Bid-Ask Spread, Trading Frequency, and Liquidity Ratio variables. To strengthen our findings, we employ Volatility Models of ARCH and GARCH, as well as JSX daily, weekly, and monthly time series data. Our findings reveal that the observed variables are able to explain volatility magnitude of JSX in terms of liquidity. Volatility model incorporating Trading Frequency variable with monthly data is found the most suitable model for measuring liquidity of JSX.

Suggested Citation

  • Erie Febrian & Aldrin Herwany, 2009. "Liquidity Measurement Based on Bid-Ask Spread, Trading Frequency, and Liquidity Ratio: The Use of GARCH Model on Jakarta Stock Exchange (JSX)," Working Papers in Economics and Development Studies (WoPEDS) 200910, Department of Economics, Padjadjaran University, revised Sep 2009.
    • Handle: RePEc:unp:wpaper:200910
    as

    Download full text from publisher

    File URL: http://lp3e.fe.unpad.ac.id/wopeds/200910.pdf
    File Function: First version, 2009
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Elena Andreou, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 290-318.
    2. Michael Cooper & Huseyin Gulen, 2006. "Is Time-Series-Based Predictability Evident in Real Time?," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1263-1292, May.
    3. Chris Brooks, 2005. "Autoregressive Conditional Kurtosis," Journal of Financial Econometrics, Oxford University Press, vol. 3(3), pages 399-421.
    4. Ané, 2005. "Do Power GARCH models really improve value-at-risk forecasts?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 29(3), pages 337-358, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Boško Živković & Jelena Minović, 2010. "Illiquidity of Frontier Financial Market: Case of Serbia," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 57(3), pages 349-367, September.

    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. Ergün, A. Tolga & Jun, Jongbyung, 2010. "Time-varying higher-order conditional moments and forecasting intraday VaR and Expected Shortfall," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 264-272, August.
    2. repec:wyi:journl:002087 is not listed on IDEAS
    3. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," JRFM, MDPI, vol. 7(2), pages 1-30, June.
    4. Leopoldo Catania & Stefano Grassi, 2017. "Modelling Crypto-Currencies Financial Time-Series," CEIS Research Paper 417, Tor Vergata University, CEIS, revised 11 Dec 2017.
    5. Eric Beutner & Julia Schaumburg & Barend Spanjers, 2024. "Bootstrapping GARCH Models Under Dependent Innovations," Tinbergen Institute Discussion Papers 24-008/III, Tinbergen Institute.
    6. David E. Allen & Michael McAleer & Marcel Scharth, 2009. "Realized Volatility Risk," CARF F-Series CARF-F-197, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, revised Jan 2010.
    7. Nguyen, Hoang & Virbickaitė, Audronė, 2023. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Energy Economics, Elsevier, vol. 124(C).
    8. Lim, Kian Guan & Chen, Ying & Yap, Nelson K.L., 2019. "Intraday information from S&P 500 Index futures options," Journal of Financial Markets, Elsevier, vol. 42(C), pages 29-55.
    9. Elena Andreou & Eric Ghysels, 2004. "Monitoring for Disruptions in Financial Markets," CIRANO Working Papers 2004s-26, CIRANO.
    10. Liu, Xiaochun & Luger, Richard, 2015. "Unfolded GARCH models," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 186-217.
    11. Yang (Greg) Hou & Mark Holmes, 2020. "Do higher order moments of return distribution provide better decisions in minimum-variance hedging? Evidence from US stock index futures," Australian Journal of Management, Australian School of Business, vol. 45(2), pages 240-265, May.
    12. Hou, Yang (Greg) & Li, Steven, 2020. "Volatility and skewness spillover between stock index and stock index futures markets during a crash period: New evidence from China," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 166-188.
    13. Cooper, Michael J. & Gubellini, Stefano, 2011. "The critical role of conditioning information in determining if value is really riskier than growth," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 289-305, March.
    14. Al Rahahleh, Naseem & Bhatti, M. Ishaq, 2017. "Co-movement measure of information transmission on international equity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 119-131.
    15. Bruno Feunou & Mohammad R. Jahan-Parvar & Roméo Tédongap, 2016. "Which parametric model for conditional skewness?," The European Journal of Finance, Taylor & Francis Journals, vol. 22(13), pages 1237-1271, October.
    16. Chen, Bin & Hong, Yongmiao, 2014. "A unified approach to validating univariate and multivariate conditional distribution models in time series," Journal of Econometrics, Elsevier, vol. 178(P1), pages 22-44.
    17. Simin, Timothy, 2008. "The Poor Predictive Performance of Asset Pricing Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(2), pages 355-380, June.
    18. Sylvia J. Soltyk & Felix Chan, 2023. "Modeling time‐varying higher‐order conditional moments: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 33-57, February.
    19. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    20. Delgado, Miguel A. & Song, Xiaojun, 2018. "Nonparametric tests for conditional symmetry," Journal of Econometrics, Elsevier, vol. 206(2), pages 447-471.
    21. Luc, BAUWENS & Arie, PREMINGER & Jeroen, ROMBOUTS, 2006. "Regime switching GARCH models," Discussion Papers (ECON - Département des Sciences Economiques) 2006006, Université catholique de Louvain, Département des Sciences Economiques.

    More about this item

    Keywords

    Bid-Ask Spread; Trading Frequency; Liquidity Ratio; and ARCH/GARCH;
    All these keywords.

    JEL classification:

    • G0 - Financial Economics - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:unp:wpaper:200910. 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: Arief Anshory Yusuf (email available below). General contact details of provider: https://edirc.repec.org/data/lppadid.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.