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Allocative efficiency and traders' protection under zero intelligence behavior

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Author Info

  • Marco LiCalzi

    ()
    (Department of Applied Mathematics, University of Venice)

  • Lucia Milone

    ()
    (Advanced School of Economics, University of Venice)

  • Paolo Pellizzari

    ()
    (Department of Applied Mathematics, University of Venice)

Abstract

This paper studies the continuous double auction from the point of view of market engineering: we tweak a resampling rule often used for this exchange protocol and search for an improved design. We assume zero intelligence trading as a lower bound for more robust behavioral rules and look at allocative efficiency, as well as three subordinate performance criteria: mean spread, cancellation rate, and traders' protection. This latter notion measures the ability of a protocol to help traders capture their share of the competitive equilibrium profits. We consider two families of resampling rules and obtain the following results. Full resampling is not necessary to attain high allocative efficiency, but fine-tuning the resampling rate is important. The best allocative performances are similar across the two families. However, if the market designer adds any of the other three criteria as a subordinate goal, then a resampling rule based on a price band around the best quotes is superior.

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File URL: http://virgo.unive.it/wpideas/storage/2008wp168.pdf
File Function: First version, 2008
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Bibliographic Info

Paper provided by Department of Applied Mathematics, Università Ca' Foscari Venezia in its series Working Papers with number 168.

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Length: 24 pages
Date of creation: Oct 2008
Date of revision: Nov 2009
Publication status: Published in H. Dawid and W. Semmler (eds.), Computational Methods in Economic Dynamics, Springer, 5-28
Handle: RePEc:vnm:wpaper:168

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Keywords: market engineering; trading protocols; competitive share; exchange market;

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References

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  1. Dhananjay (Dan) K. Gode & Shyam Sunder, 2000. "Double Auction Dynamics: Structural Effects Of Non-Binding Price Controls," Yale School of Management Working Papers ysm1, Yale School of Management.
  2. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011 Elsevier.
  3. 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.
  4. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-37, February.
  5. Mirowski, Philip, 2007. "Markets come to bits: Evolution, computation and markomata in economic science," Journal of Economic Behavior & Organization, Elsevier, vol. 63(2), pages 209-242, June.
  6. Hurwicz, Leonid & Radner, Roy & Reiter, Stanley, 1975. "A Stochastic Decentralized Resource Allocation Process: Part II," Econometrica, Econometric Society, vol. 43(3), pages 363-93, May.
  7. Marco LiCalzi & Paolo Pellizzari, 2008. "Zero-Intelligence Trading without Resampling," Working Papers 164, Department of Applied Mathematics, Università Ca' Foscari Venezia.
  8. Gul, Faruk & Lundholm, Russell, 1995. "Endogenous Timing and the Clustering of Agents' Decisions," Journal of Political Economy, University of Chicago Press, vol. 103(5), pages 1039-66, October.
  9. Smith, Vernon L, 1982. "Microeconomic Systems as an Experimental Science," American Economic Review, American Economic Association, vol. 72(5), pages 923-55, December.
  10. Paul Brewer & Maria Huang & Brad Nelson & Charles Plott, 2002. "On the Behavioral Foundations of the Law of Supply and Demand: Human Convergence and Robot Randomness," Experimental Economics, Springer, vol. 5(3), pages 179-208, December.
  11. Sean Crockett & Stephen Spear & Shyam Sunder, 1899. "Learning Competitive Equilibrium," GSIA Working Papers 2003-E18, Carnegie Mellon University, Tepper School of Business.
  12. Alvin E. Roth, 2002. "The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics," Econometrica, Econometric Society, vol. 70(4), pages 1341-1378, July.
  13. LiCalzi, Marco & Pellizzari, Paolo, 2007. "Simple market protocols for efficient risk sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(11), pages 3568-3590, November.
  14. Marco LiCalzi & Paolo Pellizzari, 2006. "The allocative effectiveness of market protocols under intelligent trading," Working Papers 134, Department of Applied Mathematics, Università Ca' Foscari Venezia.
  15. Zhan, Wenjie & Friedman, Daniel, 2007. "Markups in double auction markets," Journal of Economic Dynamics and Control, Elsevier, vol. 31(9), pages 2984-3005, September.
  16. Arifovic, Jasmina & Ledyard, John, 2007. "Call market book information and efficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1971-2000, June.
  17. Gode, Dhananjay K & Sunder, Shyam, 1997. "What Makes Markets Allocationally Efficient?," The Quarterly Journal of Economics, MIT Press, vol. 112(2), pages 603-30, May.
  18. Sunder, S., 1992. "Lower Bounds for Efficiency of Surplus Extraction in Double Auctions," GSIA Working Papers 1992-17, Carnegie Mellon University, Tepper School of Business.
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Cited by:
  1. Roberto Cervone & Stefano Galavotti & Marco LiCalzi, 2009. "Symmetric Equilibria in Double Auctions with Markdown Buyers and Markup Sellers," Working Papers 187, Department of Applied Mathematics, Università Ca' Foscari Venezia.

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