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The Impact of Imitation on Long Memory in an Order-Driven Market

  • Blake LeBaron

    (International Business School, Brandeis University, 415 South Street, Mailstop 32, Waltham, MA 02453-2728, USA. E-mails: blebaron@brandeis.edu, www.brandeis.edu/ blebaron)

  • Ryuichi Yamamoto

    (Department of International Business, National Chengchi University, 64 Sec.2, Zhi-Nan Road, Taipei, Taiwan)

Recent research has documented that learning and evolution are capable of generating many well-known features in financial times series. We extend the results of LeBaron and Yamamoto (2007) to explore the impact of varying amounts of imitation and agent learning in a simple order-driven market. We show that in our framework, imitation is critical to the generation of long memory persistence in many financial time series. This shows that imitation across trader behavior is probably crucial for understanding the dynamics of prices and trading volume. Eastern Economic Journal (2008) 34, 504–517. doi:10.1057/eej.2008.32

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Article provided by Palgrave Macmillan in its journal Eastern Economic Journal.

Volume (Year): 34 (2008)
Issue (Month): 4 ()
Pages: 504-517

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Handle: RePEc:pal:easeco:v:34:y:2008:i:4:p:504-517
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  1. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
  2. Blake LeBaron, 1999. "Evolution and Time Horizons in an Agent-Based Stock Market," Computing in Economics and Finance 1999 1342, Society for Computational Economics.
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