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Grafting of higher-order correlations of real financial markets into herding models

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  • Ahn, Sanghyun
  • Lim, Gyuchang
  • Kim, Sooyong
  • Kim, Kyungsik

Abstract

In this work, we graft the volatility clustering observed in empirical financial time series into the Equiluz and Zimmermann (EZ) model, which was introduced to reproduce the herding behaviors of a financial time series. The original EZ model failed to reproduce the empirically observed power-law exponents of real financial data. The EZ model ordinarily produces a more fat-tailed distribution compared to real data, and a long-range correlation of absolute returns that underlie the volatility clustering. As it is not appropriate to capture the empirically observed correlations in a modified EZ model, we apply a sorting method to incorporate the nonlinear correlation structure of a real financial time series into the generated returns. By doing so, we observe that the slow convergence of distribution of returns is well established for returns generated from the EZ model and its modified version. It is also found that the modified EZ model leads to a less fat-tailed distribution.

Suggested Citation

  • Ahn, Sanghyun & Lim, Gyuchang & Kim, Sooyong & Kim, Kyungsik, 2009. "Grafting of higher-order correlations of real financial markets into herding models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(15), pages 3195-3201.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:15:p:3195-3201
    DOI: 10.1016/j.physa.2009.04.005
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    References listed on IDEAS

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    1. Johnson, Neil F. & Jefferies, Paul & Hui, Pak Ming, 2003. "Financial Market Complexity," OUP Catalogue, Oxford University Press, number 9780198526650.
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    Cited by:

    1. Ahn, Sanghyun & Lim, G.C. & Kim, S.H. & Kim, Soo Yong & Yoon, Kwon Youb & Stanfield, Joseph Lee & Kim, Kyungsik, 2011. "Analysis of stock prices of mining business," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(12), pages 2340-2349.

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    Keywords

    EZ model; Correlation; KOSPI; KRW-USD;
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