JMF> Vol.3 No.2A, April 2013

Design of Financial Market Regulations against Large Price Fluctuations Using by Artificial Market Simulations

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ABSTRACT

We built an artificial market model and compared effects of price variation limits, short selling regulations and up-tick rules. In the case without the regulations, the price fell to below a fundamental value when an economic crush occurred. On the other hand, in the case with the regulations, this overshooting did not occur. However, the short selling regulation and the up-tick rule caused the trading prices to be higher than the fundamental value. We also surveyed an adequate limitation price range and an adequate limitation time span for the price variation limit and found a parameters’ condition of the price variation limit to prevent the over-shorts. We also showed the limitation price range should be bigger than a volatility calculated by the limitation time span.

Cite this paper

T. Mizuta, K. Izumi, I. Yagi and S. Yoshimura, "Design of Financial Market Regulations against Large Price Fluctuations Using by Artificial Market Simulations," Journal of Mathematical Finance, Vol. 3 No. 2A, 2013, pp. 15-22. doi: 10.4236/jmf.2013.32A003.

References

[1] Tokyo Stock Exchange, “Guide to TSE Trading Methodology,” 2012.
[2] B. LeBaron, “Agent-Based Computational Finance,” Handbook of Computational Economics, Vol. 2, 2006, pp. 1187-1233. doi:10.1016/S1574-0021(05)02024-1
[3] S.-H. Chen, C.-L. Chang and Y.-R. Du, “Agent-Based Economic Models and Econometrics,” The Knowledge Engineering Review, Vol. 27, No. 2, 2009, pp. 187-219.
[4] I. Yagi, T. Mizuta and K. Izumi, “A Study on the Effectiveness of Shortselling Regulation Using Artificial Markets,” Evolutionary and Institutional Economics Review, Vol. 7, No. 1, 2010, pp. 113-132.
[5] I. Yagi, T. Mizuta and K. Izumi, “A Study on the Market Impact of Short-Selling Regulation Using Artificial Markets,” In: Q. Bai and N. Fukuta, Eds., Advances in Practical Multi-Agent Systems, ser. Studies in Computational Intelligence, Vol. 325, Springer, Berlin, Heidelberg, 2011, pp. 217-231.
[6] F. Westerhoff, “The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies,” Jahrbucher Fur Nationalokonomie Und Statistik, Vol. 228, No. 2, 2008, p. 195.
[7] S. Thurner, J. Farmer and J. Geanakoplos, “Leverage Causes Fat Tails and Clustered Volatility,” Quantitative Finance, Vol. 12, No. 5, 2012, pp. 695-707. doi:10.1080/14697688.2012.674301
[8] S. Kobayashi and T. Hashimoto, “Benefits and Limits of Circuit Breaker: Institutional Design Using Artificial Futures Market,” Evolutionary and Institutional Economics Review, Vol. 7, No. 2, 2011, pp. 355-372.
[9] C. Yeh and C. Yang, “Examining the Effectiveness of Price Limits in an Artificial Stock Market,” Journal of Economic Dynamics and Control, Vol. 34, No. 10, 2010, pp. 2089-2108. doi:10.1016/j.jedc.2010.05.015
[10] T. Mizuta, K. Izumi and S. Yoshimura, “Price Variation Limits and Financial Market Bubbles: Artificial Market Simulations with Agents’ Learning Process,” 2013 IEEE Symposium Series on Computational Intelligence for Financial Engineering Economics, Singapore City, April 2013, pp. 1-7. http://www.ntu.edu.sg/home/epnsugan/index_files/SSCI2013/index.html
[11] C. Chiarella, G. Iori and J. Perelló, “The Impact of Heterogeneous Trading Rules on the limit order book and Order Flows,” Journal of Economic Dynamics and Control, Vol. 33, No. 3, 2009, pp. 525-537. doi:10.1016/j.jedc.2008.08.001
[12] D. Friedman, “The Double Auction Market Institution: A Survey,” In: D. Friedman and J. Rust, Eds., The Double Auction Market: Institutions, Theories, and Evidence, Addison-Wesley, Boston, 1993, pp. 3-25.
[13] B. Mandelbrot, “The Variation of Certain Speculative Prices,” The Journal of Business, Vol. 36, No. 4, 1963, pp. 394-419. doi:10.1086/294632
[14] R. Cont, “Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues,” Quantitative Finance, Vol. 1, 2001, pp. 223-236.
[15] B. Mandelbrot, “Statistical Methodology for Nonperiodic Cycles: From the Covariance to R/S Analysis,” Annals of Economic and Social Measurement, Vol. 1, No. 3, 1972, pp. 259-290.
[16] M. Sewell, “Characterization of Financial Time Series,” 2006.
[17] G. McQueen and S. Thorley, “Bubbles, Stock Returns, and Duration Dependence,” Journal of Financial and Quantitative Analysis, Vol. 29, No. 3, 1994, pp. 379-401.
[18] K. Chan, G. McQueen and S. Thorley, “Are There Rational Speculative Bubbles in Asian Stock Markets?” Pacific-Basin Finance Journal, Vol. 6, No. 1-2, 1998, pp. 125-151.
[19] I. Yagi, T. Mizuta and K. Izumi, “A Study on the Reversal Mechanism for Large Stock Price Declines Using Artificial Markets,” 2012 IEEE Conference on Computational Intelligence for Financial Engineering Economics, New York, March 2012, pp. 1-7. http://www.ieee-cifer.org/2012/index.html
[20] J. Frankel and K. Froot, “Chartists, Fundamentalists, and Trading in the Foreign Exchange Market,” The American Economic Review, Vol. 80, No. 2, 1990, pp. 181-185.
[21] R. Yamamoto and H. Hirata, “Strategy Switching in the Japanese Stock Market,” Japan Center for Economic Research Discussion Paper, No. 135, 2012.

  
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