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Adding to the Regulator's Toolbox: Integration and Extension of Two Leading Market Models

Author

Listed:
  • Brian Tivnan
  • Matthew Koehler
  • Matthew McMahon
  • Matthew Olson
  • Neal Rothleder
  • Rajani Shenoy

Abstract

As demonstrated during the recent financial crisis, regulators require additional analytical tools to assess systemic risk in the financial sector. This paper describes one such tool; namely a novel market modeling and analysis capability. Our model builds upon two leading market models: one which emphasizes market micro-structure and another which emphasizes an ecology of trading strategies. We address a limitation of market modeling, namely the consideration of only one dominant trading strategy (i.e., long positions). Our model aligns closely with several widely held stylized facts of financial markets. And a final contribution of this work stems from our empirical analysis of the fractal nature of both empirical markets and our market model.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:1105.5439
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    References listed on IDEAS

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    1. Lux, Thomas, 1998. "The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 143-165, January.
    2. LeBaron, Blake, 2001. "Evolution And Time Horizons In An Agent-Based Stock Market," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 225-254, April.
    3. Arthur, W.B. & Holland, J.H. & LeBaron, B. & Palmer, R. & Tayler, P., 1996. "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Working papers 9625, Wisconsin Madison - Social Systems.
    4. 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.
    5. 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..
    6. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    7. Cont, Rama & Bouchaud, Jean-Philipe, 2000. "Herd Behavior And Aggregate Fluctuations In Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 4(2), pages 170-196, June.
    8. 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.
    9. Thomas Lux & Michele Marchesi, 1999. "Scaling and criticality in a stochastic multi-agent model of a financial market," Nature, Nature, vol. 397(6719), pages 498-500, February.
    10. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
    11. 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.
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    Cited by:

    1. 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.

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