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An Agent-Based Model to Explain the Emergence of Stylised Facts in Log Returns

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  • Elena Green
  • Daniel M. Heffernan

Abstract

This paper outlines an agent-based model of a simple financial market in which a single asset is available for trade by three different types of traders. The model was first introduced in the PhD thesis of one of the authors, see reference [1]. The simulated log returns are examined for the presence of the stylised facts of financial data. The features of leptokurtosis, volatility clustering and aggregational Gaussianity are especially highlighted and studied in detail. The following ingredients are found to be essential for the production of these stylised facts: the memory of noise traders who make random trade decisions; the inclusion of technical traders that trade in line with trends in the price and the inclusion of fundamental traders who know the "fundamental value" of the stock and trade accordingly. When these three basic types of traders are included log returns are produced with a leptokurtic distribution and volatility clustering as well as some further statistical features of empirical data. This enhances and broadens our understanding of the fundamental processes involved in the production of empirical data by the market.

Suggested Citation

  • Elena Green & Daniel M. Heffernan, 2019. "An Agent-Based Model to Explain the Emergence of Stylised Facts in Log Returns," Papers 1901.05053, arXiv.org.
    • Handle: RePEc:arx:papers:1901.05053
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    References listed on IDEAS

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    1. Zeyu Zheng & Zhi Qiao & Joel N. Tenenbaum & H. Eugene Stanley & Baowen Li, 2014. "Predicting market instability: New dynamics between volume and volatility," Papers 1403.5193, arXiv.org.
    2. repec:wsi:ijtafx:v:03:y:2000:i:04:n:s0219024900000826 is not listed on IDEAS
    3. Paul Jefferies & Michael Hart & Neil Johnson & P.M. Hui, 2001. "From market games to real-world markets," OFRC Working Papers Series 2001mf02, Oxford Financial Research Centre.
    4. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    5. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(1), pages 109-126, March.
    6. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    7. E.R. Grannan & G.H. Swindle, 1994. "Contrarians and Volatility Clustering," Working Papers 94-03-010, Santa Fe Institute.
    8. repec:spr:eurphb:v:20:y:2001:i:4:d:10.1007_s100510170228 is not listed on IDEAS
    9. Richmond, Peter & Mimkes, Jurgen & Hutzler, Stefan, 2013. "Econophysics and Physical Economics," OUP Catalogue, Oxford University Press, number 9780199674701.
    10. Westerfield, Randolph, 1977. "The Distribution of Common Stock Price Changes: An Application of Transactions Time and Subordinated Stochastic Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(5), pages 743-765, December.
    11. Antypas, Antonios & Koundouri, Phoebe & Kourogenis, Nikolaos, 2013. "Aggregational Gaussianity and barely infinite variance in financial returns," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 102-108.
    12. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 199-242.
    13. repec:wsi:ijtafx:v:03:y:2000:i:03:n:s0219024900000358 is not listed on IDEAS
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