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Converse trading strategies, intrinsic noise and the stylized facts of financial markets

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  • Frank Westerhoff
  • Reiner Franke

Abstract

This paper proposes a simple asset pricing model with three groups of traders: chartists who believe in the persistence of bull and bear markets, fundamentalists who bet on a reduction of the observed mispricing, and investors who follow a buy-and-hold strategy. The innovative feature of the model concerns the frequency of trading: rather than remaining constant over time, each agent in a group is only assumed to become active with a certain probability over a given market period. Depending on the trading strategy, part of this elementary kind of intrinsic noise is additive and another part is multiplicative. Using bootstrap and Monte Carlo methods, it is demonstrated that this combination can contribute to explaining the stylized facts of the daily returns on financial markets, such as volatility clustering, fat tails, and the autocorrelation patterns.

Suggested Citation

  • Frank Westerhoff & Reiner Franke, 2012. "Converse trading strategies, intrinsic noise and the stylized facts of financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 425-436, June.
  • Handle: RePEc:taf:quantf:v:12:y:2012:i:3:p:425-436
    DOI: 10.1080/14697688.2010.504224
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    References listed on IDEAS

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    Cited by:

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    3. Wei Zhang & Pengfei Wang & Xiao Li & Dehua Shen, 2018. "Some stylized facts of the cryptocurrency market," Applied Economics, Taylor & Francis Journals, vol. 50(55), pages 5950-5965, November.
    4. Martin Carolin & Westerhoff Frank, 2019. "Regulating Speculative Housing Markets via Public Housing Construction Programs: Insights from a Heterogeneous Agent Model," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(4), pages 627-660, August.
    5. Jackson, Antony & Ladley, Daniel, 2016. "Market ecologies: The effect of information on the interaction and profitability of technical trading strategies," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 270-280.
    6. Luisanna Cocco & Michele Marchesi, 2016. "Modeling and Simulation of the Economics of Mining in the Bitcoin Market," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-31, October.
    7. Luisanna Cocco & Giulio Concas & Michele Marchesi, 2017. "Using an artificial financial market for studying a cryptocurrency market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(2), pages 345-365, July.
    8. Ji, Jingru & Wang, Donghua & Xu, Dinghai, 2019. "Modelling the spreading process of extreme risks via a simple agent-based model: Evidence from the China stock market," Economic Modelling, Elsevier, vol. 80(C), pages 383-391.
    9. Schmitt, Noemi, 2018. "Heterogeneous expectations and asset price dynamics," BERG Working Paper Series 134, Bamberg University, Bamberg Economic Research Group.
    10. Zhenxi Chen, 2020. "Regional financial market bloc and spillover of the financial crisis: A heterogeneous agents approach," Manchester School, University of Manchester, vol. 88(2), pages 262-281, March.
    11. Tae-Seok Jang, 2015. "Identification of Social Interaction Effects in Financial Data," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 207-238, February.

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