[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/ofr/wpaper/15-03.html
   My bibliography  Save this paper

Market Liquidity and Heterogeneity in the Investor Decision Cycle

Author

Listed:
  • Richard Bookstaber

    (Office of Financial Research)

  • Michael D. Foley

    (University of Vermont)

  • Brian F. Tivnan

    (MITRE Corporation)

Abstract
During liquidity shocks such as occur when margin calls force the liquidation of leveraged positions, there is a widening disparity between the reaction speed of the liquidity demanders and the liquidity providers. Those who are forced to sell typically must take action within the span of a day, while those who are providing liquidity do not face similar urgency. Indeed, the flurry of activity and increased volatility of prices during the liquidity shocks might actually reduce the speed with which many liquidity providers come to the market. To analyze these dynamics, we build upon previous agent-based models of financial markets to develop an order-book model with heterogeneity in trader decision cycles. The model demonstrates an adherence to important stylized facts such as a leptokurtic distribution of returns, decay of autocorrelations over moderate to long time lags, and clustering volatility. We show that the heterogeneity in decision cycles can increase the severity of market shocks, and even absent a shock can have notable effects on the stochastic properties of market prices.

Suggested Citation

  • Richard Bookstaber & Michael D. Foley & Brian F. Tivnan, 2015. "Market Liquidity and Heterogeneity in the Investor Decision Cycle," Working Papers 15-03, Office of Financial Research, US Department of the Treasury.
    • Handle: RePEc:ofr:wpaper:15-03
    as

    Download full text from publisher

    File URL: https://financialresearch.gov/working-papers/files/OFRwp-2015-03_Market-Liquidity-and-Heterogeneity-in-Investor-Decision-Cycle.pdf
    File Function: First version, 2015
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stefan Thurner & J. Doyne Farmer & John Geanakoplos, 2012. "Leverage causes fat tails and clustered volatility," Quantitative Finance, Taylor & Francis Journals, vol. 12(5), pages 695-707, February.
    2. Brian Tivnan & Matthew Koehler & Matthew McMahon & Matthew Olson & Neal Rothleder & Rajani Shenoy, 2011. "Adding to the Regulator's Toolbox: Integration and Extension of Two Leading Market Models," Papers 1105.5439, arXiv.org.
    3. Iori, G. & Daniels, M.G. & Farmer, J.D. & Gillemot, L. & Krishnamurthy, S. & Smith, E., 2003. "An analysis of price impact function in order-driven markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 146-151.
    4. Omar A. Guerrero & Robert L. Axtell, 2011. "Using Agentization for Exploring Firm and Labor Dynamics," Lecture Notes in Economics and Mathematical Systems, in: Sjoukje Osinga & Gert Jan Hofstede & Tim Verwaart (ed.), Emergent Results of Artificial Economics, pages 139-150, Springer.
    5. J. Doyne Farmer & Paolo Patelli & Ilija I. Zovko, 2003. "The Predictive Power of Zero Intelligence in Financial Markets," Papers cond-mat/0309233, arXiv.org, revised Feb 2004.
    6. Vincent Darley & Alexander V Outkin, 2007. "Learning, Evolution and Tick Size Effects," World Scientific Book Chapters, in: A Nasdaq Market Simulation Insights on a Major Market from the Science of Complex Adaptive Systems, chapter 5, pages 81-87, World Scientific Publishing Co. Pte. Ltd..
    7. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    8. Maslov, Sergei, 2000. "Simple model of a limit order-driven market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 278(3), pages 571-578.
    9. Robert Axtell & Robert Axelrod & Joshua M. Epstein & Michael D. Cohen, 1995. "Aligning Simulation Models: A Case Study and Results," Working Papers 95-07-065, Santa Fe Institute.
    10. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    11. Darrell Duffie, 2010. "Presidential Address: Asset Price Dynamics with Slow‐Moving Capital," Journal of Finance, American Finance Association, vol. 65(4), pages 1237-1267, August.
    12. Richard Bookstaber, 2012. "Using Agent-Based Models for Analyzing Threats to Financial Stability," Working Papers 12-03, Office of Financial Research, US Department of the Treasury.
    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. Mark D. Flood & John C. Liechty & Thomas Piontek, 2015. "Systemwide Commonalities in Market Liquidity," Working Papers 15-11, Office of Financial Research, US Department of the Treasury.
    2. Donovan Platt & Tim Gebbie, 2016. "Can Agent-Based Models Probe Market Microstructure?," Papers 1611.08510, arXiv.org, revised Aug 2017.
    3. Braun-Munzinger, Karen & Liu, Zijun & Turrell, Arthur, 2016. "An agent-based model of dynamics in corporate bond trading," Bank of England working papers 592, Bank of England.

    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. Brian Tivnan & Matthew Koehler & Matthew McMahon & Matthew Olson & Neal Rothleder & Rajani Shenoy, 2011. "Adding to the Regulator's Toolbox: Integration and Extension of Two Leading Market Models," Papers 1105.5439, arXiv.org.
    2. Richard Bookstaber & Mark Paddrik, 2015. "An Agent-Based Model of Liquidity," Working Papers 15-18, Office of Financial Research, US Department of the Treasury.
    3. Roberto Mota Navarro & Hern'an Larralde Ridaura, 2016. "A detailed heterogeneous agent model for a single asset financial market with trading via an order book," Papers 1601.00229, arXiv.org, revised Jul 2016.
    4. Leal, Sandrine Jacob & Napoletano, Mauro, 2019. "Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 15-41.
    5. Sandrine Jacob Leal & Mauro Napoletano, 2017. "Market Stability vs. Market Resilience: Regulatory Policies Experiments in an Agent-Based Model with Low- and High-Frequency Trading," Post-Print hal-01768876, HAL.
    6. Alessio Emanuele Biondo, 2018. "Order book microstructure and policies for financial stability," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 35(1), pages 196-218, March.
    7. Richard Bookstaber & Mark Paddrik & Brian Tivnan, 2018. "An agent-based model for financial vulnerability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(2), pages 433-466, July.
    8. Biondo, Alessio Emanuele, 2017. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics Discussion Papers 2017-104, Kiel Institute for the World Economy (IfW Kiel).
    9. Scheffknecht, Lukas & Geiger, Felix, 2011. "A behavioral macroeconomic model with endogenous boom-bust cycles and leverage dynamcis," FZID Discussion Papers 37-2011, University of Hohenheim, Center for Research on Innovation and Services (FZID).
    10. Wang, Yougui & Stanley, H.E., 2009. "Statistical approach to partial equilibrium analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(7), pages 1173-1180.
    11. Gu, Gao-Feng & Chen, Wei & Zhou, Wei-Xing, 2008. "Empirical regularities of order placement in the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3173-3182.
    12. Sandrine Jacob Leal & Mauro Napoletano & Andrea Roventini & Giorgio Fagiolo, 2016. "Rock around the clock: An agent-based model of low- and high-frequency trading," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 49-76, March.
    13. Mark Paddrik & Roy Hayes & William Scherer & Peter Beling, 2017. "Effects of limit order book information level on market stability metrics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(2), pages 221-247, July.
    14. Olivier Brandouy & Angelo Corelli & Iryna Veryzhenko & Roger Waldeck, 2012. "A re-examination of the “zero is enough” hypothesis in the emergence of financial stylized facts," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 223-248, October.
    15. repec:spo:wpmain:info:hdl:2441/f6h8764enu2lskk9p4oq9ig8k is not listed on IDEAS
    16. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    17. Mike, Szabolcs & Farmer, J. Doyne, 2008. "An empirical behavioral model of liquidity and volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 200-234, January.
    18. Alessio Emanuele Biondo, 2019. "Order book modeling and financial stability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 469-489, September.
    19. Ivan Jericevich & Patrick Chang & Tim Gebbie, 2021. "Simulation and estimation of an agent-based market-model with a matching engine," Papers 2108.07806, arXiv.org, revised Aug 2021.
    20. Tzouras, Spilios & Anagnostopoulos, Christoforos & McCoy, Emma, 2015. "Financial time series modeling using the Hurst exponent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 425(C), pages 50-68.
    21. Jean-Philippe Bouchaud & J. Doyne Farmer & Fabrizio Lillo, 2008. "How markets slowly digest changes in supply and demand," Papers 0809.0822, arXiv.org.

    More about this item

    Keywords

    Liquidity;

    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:ofr:wpaper:15-03. 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: Gregory Feldberg (email available below). General contact details of provider: https://edirc.repec.org/data/ofrgvus.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.