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Dark Pool Usage and Equity Market Volatility

  • Yibing XiongEmail author
  • Takashi Yamada
  • Takao Terano
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

An agent-based simulation is conducted to explore the relationship between dark pool usage and equity market volatility. We model an order-driven stock market populated by liquidity traders who have different, but fixed, degrees of dark pool usage. The deviation between the order execution prices of different traders and the volume weighted average price of the market is calculated in an attempt to measure the effect of dark pool usage on price volatility. By simulating the stock market under different conditions, we find that the use of the dark pool enhances market stability. This volatility-decreasing effect is shown to become stronger as the usage of the dark pool increases, when the proportion of market orders is lower, and when market volatility is lower.

Keywords

Dark pool Market volatility Agent-based model Behavioral economics Order-driven market 

References

  1. 1.
    Buti, S., Rindi, B., & Werner, I. M. (2011). Dark Pool Trading Strategies. Charles A Dice Center Working Paper (2010-6).Google Scholar
  2. 2.
    Buti S, Rindi, B., & Werner, I. M. (2011). Diving into Dark Pools. Charles A Dice Center Working Paper (2010-10).Google Scholar
  3. 3.
    Challet, D., & Stinchcombe, R. (2001). Analyzing and modeling 1+ 1D markets. Physica A: Statistical Mechanics and its Applications, 300(1), 285–299.CrossRefGoogle Scholar
  4. 4.
    Cheridito, P., & Sepin, T. (2014). Optimal trade execution with a dark pool and adverse selection. Available at SSRN 2490234.Google Scholar
  5. 5.
    Degryse, H., De Jong, F., Van Kervel, V. (2011). The impact of dark and visible fragmentation on market quality. SSRN eLibrary.Google Scholar
  6. 6.
    Farmer, J. D., & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460(7256), 685–686.CrossRefGoogle Scholar
  7. 7.
    Farmer, J. D., Patelli, P., & Zovko, I. I. (2005). The predictive power of zero intelligence in financial markets. Proceedings of the National Academy of Sciences of the United States of America, 102(6), 2254–2259.CrossRefGoogle Scholar
  8. 8.
    Gode, D. K., & Sunder, S. (1993). Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy, 101, 119–137.CrossRefGoogle Scholar
  9. 9.
    Maslov, S. (2000). Simple model of a limit order-driven market. Physica A: Statistical Mechanics and Its Applications, 278(3), 571–578.CrossRefGoogle Scholar
  10. 10.
    Mizuta, T., Matsumoto, W., Kosugi, S., Izumi, K., Kusumoto, T., & Yoshimura, S. (2014). Do dark pools stabilize markets and reduce market impacts? Investigations using multi-agent simulations. In 2104 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (pp. 71–76). New York: IEEE.Google Scholar
  11. 11.
    Mo, S. Y. K., Paddrik, M., & Yang, S. Y. (2013). A study of dark pool trading using an agent-based model. In 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (pp. 19–26). New York: IEEE.CrossRefGoogle Scholar
  12. 12.
    Nimalendran, M., & Ray, S. (2011), Informed trading in dark pools. SSRN eLibrary.Google Scholar
  13. 13.
    Preece, R., & Rosov, S. (2014). Dark trading and equity market quality. Financial Analysts Journal, 70(6), 33–48.CrossRefGoogle Scholar
  14. 14.
    Preis, T., Golke, S., Paul, W., & Schneider, J. J. (2006). Multi-agent-based order book model of financial markets. Europhysics Letters, 75(3), 510.CrossRefGoogle Scholar
  15. 15.
    Ray, S. (2010). A match in the dark: understanding crossing network liquidity. Available at SSRN 1535331.Google Scholar
  16. 16.
    Ready, M. J. (2010). Determinants of volume in dark pools. Working Paper, University of Wisconsin-Madison.Google Scholar
  17. 17.
    Satchell, S., & Knight, J. (2011). Forecasting volatility in the financial markets. Oxford: Butterworth-Heinemann.Google Scholar
  18. 18.
    Securities and Exchange Commission. (2010). Concept release on equity market structure. Federal Register, 75(13), 3594–3614.Google Scholar
  19. 19.
    Ye, M. (2011). A glimpse into the dark: Price formation, transaction cost and market share of the crossing network. Transaction Cost and Market Share of the Crossing Network June 9 (2011).Google Scholar
  20. 20.
    Zhu, H. (2013). Do dark pools harm price discovery? Review of Financial Studies. 27(3), 747–789.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Tokyo Institute of TechnologyYokohama, KanagawaJapan

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