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Impact Analysis of Financial Regulation on Multi-Asset Markets Using Artificial Market Simulations

Author

Listed:
  • Masanori Hirano

    (Department of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan)

  • Kiyoshi Izumi

    (Department of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan)

  • Takashi Shimada

    (Department of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
    Mathematics and Informatics Center, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan)

  • Hiroyasu Matsushima

    (Department of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan)

  • Hiroki Sakaji

    (Department of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan)

Abstract

In this study, we assessed the impact of capital adequacy ratio (CAR) regulation in the Basel regulatory framework. This regulation was established to make the banking network robust. However, a previous work argued that CAR regulation has a destabilization effect on financial markets. To assess impacts such as destabilizing effects, we conducted simulations of an artificial market, one of the computer simulations imitating real financial markets. In the simulation, we proposed and used a new model with continuous double auction markets, stylized trading agents, and two kinds of portfolio trading agents. Both portfolio trading agents had trading strategies incorporating Markowitz’s portfolio optimization. Additionally, one type of portfolio trading agent was under regulation. From the simulations, we found that portfolio optimization as each trader’s strategy stabilizes markets, and CAR regulation destabilizes markets in various aspects. These results show that CAR regulation can have negative effects on asset markets. As future work, we should confirm these effects empirically and consider how to balance between both positive and negative aspects of CAR regulation.

Suggested Citation

  • Masanori Hirano & Kiyoshi Izumi & Takashi Shimada & Hiroyasu Matsushima & Hiroki Sakaji, 2020. "Impact Analysis of Financial Regulation on Multi-Asset Markets Using Artificial Market Simulations," JRFM, MDPI, vol. 13(4), pages 1-20, April.
  • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:4:p:75-:d:346788
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    References listed on IDEAS

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    1. O. Hermsen, 2010. "Does Basel II destabilize financial markets? An agent-based financial market perspective," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(1), pages 29-40, January.
    2. Scott Moss & Bruce Edmonds, 2005. "Towards Good Social Science," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-13.
    3. Carl Chiarella & Giulia Iori, 2002. "A simulation analysis of the microstructure of double auction markets," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 346-353.
    4. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
    5. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    6. 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.
    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. Takanobu Mizuta, 2019. "An agent-based model for designing a financial market that works well," Papers 1906.06000, arXiv.org.
    9. Daigo Tashiro & Hiroyasu Matsushima & Kiyoshi Izumi & Hiroki Sakaji, 2019. "Encoding of high-frequency order information and prediction of short-term stock price by deep learning," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1499-1506, September.
    10. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
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    Cited by:

    1. Masanori Hirano & Ryosuke Takata & Kiyoshi Izumi, 2023. "PAMS: Platform for Artificial Market Simulations," Papers 2309.10729, arXiv.org.

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