IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/43183.html
   My bibliography  Save this paper

Auction Market System in Electronic Security Trading Platform

Author

Listed:
  • Li, Xi Hao

Abstract

Under the background of the electronic security trading platform Xetra operated by Frankfurt Stock Exchange, we consider the Xetra auction market system (XAMS) from `bottom-up', which the interaction among heterogeneous traders and Xetra auction market mechanism generates non-equilibrium price dynamics. First we develop an integrative framework that serves as general guidance for analyzing the economic system from `bottom-up' and for seamlessly transferring the economic system into the corresponding agent-based model. Then we apply this integrative framework to construct the agent-based model of XAMS. By conducting market experiments with the computer implementation of the agent-based model of XAMS, we investigate the role of the price setter who assumes its trading behavior can manipulate the market price. The main finding is that the introduction of the price setter in the setting of XAMS improves market efficiency while does not significantly influence price volatility of the market.

Suggested Citation

  • Li, Xi Hao, 2012. "Auction Market System in Electronic Security Trading Platform," MPRA Paper 43183, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:43183
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/43183/1/MPRA_paper_43183.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    2. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    3. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    4. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    5. Jason Potts, 2000. "The New Evolutionary Microeconomics," Books, Edward Elgar Publishing, number 2258.
    6. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    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. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046.
    2. Richard Holt & J. Barkley Rosser & David Colander, 2011. "The Complexity Era in Economics," Review of Political Economy, Taylor & Francis Journals, vol. 23(3), pages 357-369.
    3. He, Xue-Zhong & Li, Youwei, 2015. "Testing of a market fraction model and power-law behaviour in the DAX 30," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 1-17.
    4. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    5. Mauro Napoletano, 2018. "A Short Walk on the Wild Side: Agent-Based Models and their Implications for Macroeconomic Analysis," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(3), pages 257-281.
    6. Furtado, Bernardo Alves & Eberhardt, Isaque Daniel Rocha, 2015. "Modelo espacial simples da economia: uma proposta teórico-metodológica [A simple spatial economic model: a proposal]," MPRA Paper 67005, University Library of Munich, Germany.
    7. Li, Xi Hao, 2013. "Standardization for Agent-based Modeling in Economics," MPRA Paper 47396, University Library of Munich, Germany.
    8. Elena G. Irwin, 2010. "New Directions For Urban Economic Models Of Land Use Change: Incorporating Spatial Dynamics And Heterogeneity," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 65-91, February.
    9. Bernardo A. Furtado & Miguel A. Fuentes & Claudio J. Tessone, 2019. "Policy Modeling and Applications: State-of-the-Art and Perspectives," Complexity, Hindawi, vol. 2019, pages 1-11, February.
    10. Min Zheng & Duo Wang & Xue-Zhong He, 2009. "Asymmetry of technical analysis and market price volatility," Published Paper Series 2009-6, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    11. repec:hal:spmain:info:hdl:2441/2qdhj5485p93jrnf08s1meeap9 is not listed on IDEAS
    12. 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.
    13. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    14. Peter Winker & Manfred Gilli & Vahidin Jeleskovic, 2007. "An objective function for simulation based inference on exchange rate data," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 2(2), pages 125-145, December.
    15. Biondo, Alessio Emanuele, 2018. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-21.
    16. Tatiana Filatova & Dawn C. Parker & Anne van der Veen, 2009. "Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-3.
    17. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2011. "The dynamic behaviour of asset prices in disequilibrium: a survey," International Journal of Behavioural Accounting and Finance, Inderscience Enterprises Ltd, vol. 2(2), pages 101-139.
    18. Marcelo De Carvalho Pereira, 2014. "When Competition May Hinder Technologydiffusion: The Case Of Internet Access Services In Brazil," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 152, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    19. Robert Somogyi & Janos Vincze, 2011. "Price Rigidity and Strategic Uncertainty An Agent-based Approach," CERS-IE WORKING PAPERS 1135, Institute of Economics, Centre for Economic and Regional Studies.
    20. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2008. "Heterogeneity, Market Mechanisms, and Asset Price Dynamics," Research Paper Series 231, Quantitative Finance Research Centre, University of Technology, Sydney.
    21. Carl Chiarella & Roberto Dieci & Xue-Zhong He & Kai Li, 2013. "An evolutionary CAPM under heterogeneous beliefs," Annals of Finance, Springer, vol. 9(2), pages 185-215, May.

    More about this item

    Keywords

    agent-based modelling; computational market experiment; electronic security trading platform; Xetra; non-equilibrium priced ynamics; automatic trading;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • G1 - Financial Economics - - General Financial Markets
    • D5 - Microeconomics - - General Equilibrium and Disequilibrium
    • D6 - Microeconomics - - Welfare Economics
    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • D4 - Microeconomics - - Market Structure, Pricing, and Design

    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:pra:mprapa:43183. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.