IDEAS home Printed from https://ideas.repec.org/a/fru/finjrn/230506p94-116.html
   My bibliography  Save this article

Assessing the Margin Requirements Impact on the Russian Futures Market Liquidity

Author

Listed:
  • Artem I. Potapov

    (HSE University, Moscow, Russian Federation)

Abstract

The implementation of a margin system in derivatives markets has a positive effect on market liquidity and market pricing efficiency. At the same time, portfolio diversification and hedging are not fully taken into account when assessing margin requirements. Therefore, in order to comply with regulatory requirements, the exchange sets excessive margins. IAs Charoula Daskalaki and George Skiadopoulos have shown, overestimation of margin requirements reduces the positive effect of the existence of a margin system. However, quantification of this observation has not been presented in studies before. This paper quantifies the dependence of market liquidity on the level of margin. The study is conducted on data from futures contracts for 19 underlying assets traded on the Moscow Exchange between 2014 and 2021, using an autoregressive moving average model with exogenous factors (ARMAX). The stability of the obtained results is determined by comparing different model specifications with different sliding window sizes. The analysis not only confirmed the fact that margin requirements, which protect the exchange's capital, reduce the positive effect of implementing a margin system but also allowed to evaluate it quantitatively: a 1% increase in margin requirements in relative terms reduces trading volume from 2.5 to 7% and the volume of open positions from 0.2 to 0.9%, depending on the type of position and trader. The impact on trading volume is on average stable over time, and there are local trends and tipping points for the volume of open positions.

Suggested Citation

  • Artem I. Potapov, 2023. "Assessing the Margin Requirements Impact on the Russian Futures Market Liquidity," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 5, pages 94-116, October.
    • Handle: RePEc:fru:finjrn:230506:p:94-116
    DOI: 10.31107/2075-1990-2023-5-94-116
    as

    Download full text from publisher

    File URL: https://www.finjournal-nifi.ru/images/FILES/Journal/Archive/2023/5/statii/06_5_2023_v15.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.31107/2075-1990-2023-5-94-116?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
    ---><---

    References listed on IDEAS

    as
    1. Acharya, Viral V., 2009. "A theory of systemic risk and design of prudential bank regulation," Journal of Financial Stability, Elsevier, vol. 5(3), pages 224-255, September.
    2. Subrahmanyam, Avanidhar, 1991. "Risk Aversion, Market Liquidity, and Price Efficiency," The Review of Financial Studies, Society for Financial Studies, vol. 4(3), pages 416-441.
    3. Rama Cont & Romain Deguest & Giacomo Scandolo, 2010. "Robustness and sensitivity analysis of risk measurement procedures," Quantitative Finance, Taylor & Francis Journals, vol. 10(6), pages 593-606.
    4. Acharya, Viral & Bisin, Alberto, 2014. "Counterparty risk externality: Centralized versus over-the-counter markets," Journal of Economic Theory, Elsevier, vol. 149(C), pages 153-182.
    5. M S Narasimhan & Shalu Kalra, 2012. "The Impact of Derivative Trading on the Liquidity Beta of Underlying Stocks in India," The IUP Journal of Applied Finance, IUP Publications, vol. 18(4), pages 97-107, October.
    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. Xiong, Shi & Chen, Weidong, 2022. "A robust hybrid method using dynamic network analysis and Weighted Mahalanobis distance for modeling systemic risk in the international energy market," Energy Economics, Elsevier, vol. 109(C).
    2. Borbála Szüle, 2019. "Systemic Risk Dimensions in the Hungarian Banking and Insurance Sector," Public Finance Quarterly, State Audit Office of Hungary, vol. 64(2), pages 260-276.
    3. Xin Huang & Hao Zhou & Haibin Zhu, 2012. "Systemic Risk Contributions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 42(1), pages 55-83, October.
    4. Armstrong, Christopher & Nicoletti, Allison & Zhou, Frank S., 2022. "Executive stock options and systemic risk," Journal of Financial Economics, Elsevier, vol. 146(1), pages 256-276.
    5. Murat Cakir, 2017. "A conceptual design of "what and how should a proper macro-prudential policy framework be?" A globalistic approach to systemic risk and procuring the data needed," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Uses of central balance sheet data offices' information, volume 45, Bank for International Settlements.
    6. Sudipto Bhattacharya & Pojanart Sunirand, 2012. "Banks, Relative Performance, and Sequential Contagion," Chapters, in: The Challenge of Financial Stability, chapter 7, pages 153-170, Edward Elgar Publishing.
    7. Delis, Manthos D. & Hasan, Iftekhar & Tsionas, Efthymios G., 2015. "Firms' risk endogenous to strategic management choices," Bank of Finland Research Discussion Papers 16/2015, Bank of Finland.
    8. Duca, John V., 2013. "Did the commercial paper funding facility prevent a Great Depression style money market meltdown?," Journal of Financial Stability, Elsevier, vol. 9(4), pages 747-758.
    9. Corbet, Shaen & Larkin, Charles, 2017. "Has the uniformity of banking regulation within the European Union restricted rather than encouraged sectoral development?," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 48-65.
    10. Gabriel Desgranges & Celine Rochon, 2008. "Conformism, Public News and Market Effciency," OFRC Working Papers Series 2008fe16, Oxford Financial Research Centre.
    11. Umut c{C}etin & Albina Danilova, 2014. "Markovian Nash equilibrium in financial markets with asymmetric information and related forward-backward systems," Papers 1407.2420, arXiv.org, revised Sep 2016.
    12. Wilson, Linus & Wu, Yan Wendy, 2012. "Escaping TARP," Journal of Financial Stability, Elsevier, vol. 8(1), pages 32-42.
    13. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    14. Barrieu, Pauline & Scandolo, Giacomo, 2014. "Assessing financial model risk," LSE Research Online Documents on Economics 60084, London School of Economics and Political Science, LSE Library.
    15. Lof, Matthijs & van Bommel, Jos, 2023. "Asymmetric information and the distribution of trading volume," Journal of Corporate Finance, Elsevier, vol. 82(C).
    16. Bondarenko, Oleg, 2001. "Competing market makers, liquidity provision, and bid-ask spreads," Journal of Financial Markets, Elsevier, vol. 4(3), pages 269-308, June.
    17. Andrea Zaghini, 2014. "Bank Bonds: Size, Systemic Relevance and the Sovereign," International Finance, Wiley Blackwell, vol. 17(2), pages 161-184, June.
    18. Jokipii, Terhi & Milne, Alistair, 2011. "Bank capital buffer and risk adjustment decisions," Journal of Financial Stability, Elsevier, vol. 7(3), pages 165-178, August.
    19. Paolo Vitale, 2017. "Ambiguity-aversion in a Single Auction Market," Economics Bulletin, AccessEcon, vol. 37(3), pages 1745-1752.
    20. Katarzyna Kubiszewska, 2018. "Interdependence of Ratios in Banking Stability Pentagon (Wspolzaleznosc wskaznikow w Pieciokacie Stabilnosci Bankowej)," Research Reports, University of Warsaw, Faculty of Management, vol. 2(28), pages 65-79.

    More about this item

    Keywords

    derivatives; time series analysis; liquidity; margin requirements;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:fru:finjrn:230506:p:94-116. 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: Gennady Ageev (email available below). General contact details of provider: https://edirc.repec.org/data/frigvru.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.