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Regional Financial Markets With Common Currency

Author

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  • Weihong HUANG

    (Division of Economics, Nanyang Technological University, Singapore 637332, Singapore)

  • Zhenxi CHEN

    (Division of Economics, Nanyang Technological University, Singapore 637332, Singapore)

Abstract

With the development of globalization and regional market integration, regional markets with common currency emerge. We develop a heterogeneous agents model based on the frameworks of Day and Huang (1990) as well as Westerhoff and Dieci (2006). Two markets using same currency are populated by chartists and fundamentalists. Market linkage is established by allowing investors to trade in both markets. One of the consequences of market linkage is market pooling, in which investors from each market interact with each other and determine the price movements of the market system. The market that is more stable initially exerts stabilizing force on the market system while itself might su¤er from destabilizing effect. Market system based on the model demonstrates the capability to generate important stylized facts of financial markets, in particular the significant cross-correlation between two markets.

Suggested Citation

  • Weihong HUANG & Zhenxi CHEN, 2012. "Regional Financial Markets With Common Currency," Economic Growth Centre Working Paper Series 1210, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
  • Handle: RePEc:nan:wpaper:1210
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    References listed on IDEAS

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    1. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    2. 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.
    3. Day, Richard H. & Huang, Weihong, 1990. "Bulls, bears and market sheep," Journal of Economic Behavior & Organization, Elsevier, vol. 14(3), pages 299-329, December.
    4. Brock, William A & LeBaron, Blake D, 1996. "A Dynamic Structural Model for Stock Return Volatility and Trading Volume," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 94-110, February.
    5. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2009. "More hedging instruments may destabilize markets," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1912-1928, November.
    6. Bohm, Volker & Wenzelburger, Jan, 2005. "On the performance of efficient portfolios," Journal of Economic Dynamics and Control, Elsevier, vol. 29(4), pages 721-740, April.
    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. Donaldson, R. Glen & Kim, Harold Y., 1993. "Price Barriers in the Dow Jones Industrial Average," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(3), pages 313-330, September.
    9. He, Xue-Zhong & Westerhoff, Frank H., 2005. "Commodity markets, price limiters and speculative price dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 29(9), pages 1577-1596, September.
    10. Dieci, Roberto & Westerhoff, Frank, 2010. "Heterogeneous speculators, endogenous fluctuations and interacting markets: A model of stock prices and exchange rates," Journal of Economic Dynamics and Control, Elsevier, vol. 34(4), pages 743-764, April.
    11. Xue-Zhong He & Youwei Li, 2008. "Heterogeneity, convergence, and autocorrelations," Quantitative Finance, Taylor & Francis Journals, vol. 8(1), pages 59-79.
    12. Farmer, J. Doyne & Joshi, Shareen, 2002. "The price dynamics of common trading strategies," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 149-171, October.
    13. Balázs Égert & Evžen Kočenda, 2011. "Time-varying synchronization of European stock markets," Empirical Economics, Springer, vol. 40(2), pages 393-407, April.
    14. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
    15. Zhu, Mei & Chiarella, Carl & He, Xue-Zhong & Wang, Duo, 2009. "Does the market maker stabilize the market?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(15), pages 3164-3180.
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    Cited by:

    1. Huang, Weihong & Chen, Zhenxi, 2015. "Heterogeneous agents in multi-markets: A coupled map lattices approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 108(C), pages 3-15.
    2. Weihong HUANG & Zhenxi CHEN, 2012. "Heterogeneous Agents in Multi-markets: A Coupled Map Lattices Approach," Economic Growth Centre Working Paper Series 1211, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.

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    More about this item

    Keywords

    Financial multi-market interactions; Market integration; Market Pooling; Chaos; Heterogeneous beliefs;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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