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Modeling stylized facts for financial time series

Author

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  • Krivoruchenko, M.I.
  • Alessio, E.
  • Frappietro, V.
  • Streckert, L.J.

Abstract

Multivariate probability density functions of returns are constructed in order to model the empirical behavior of returns in a financial time series. They describe the well-established deviations from the Gaussian random walk, such as an approximate scaling and heavy tails of the return distributions, long-ranged volatility–volatility correlations (volatility clustering) and return–volatility correlations (leverage effect). The model is tested successfully to fit joint distributions of the 100+ years of daily price returns of the Dow Jones 30 Industrial Average.

Suggested Citation

  • Krivoruchenko, M.I. & Alessio, E. & Frappietro, V. & Streckert, L.J., 2004. "Modeling stylized facts for financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 263-266.
    • Handle: RePEc:eee:phsmap:v:344:y:2004:i:1:p:263-266
    DOI: 10.1016/j.physa.2004.06.129
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    Citations

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    Cited by:

    1. Covarrubias, Guillermo & Ewing, Bradley T. & Hein, Scott E. & Thompson, Mark A., 2006. "Modeling volatility changes in the 10-year Treasury," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 737-744.
    2. Kovačić, Zlatko, 2007. "Forecasting volatility: Evidence from the Macedonian stock exchange," MPRA Paper 5319, University Library of Munich, Germany.
    3. Andrei Leonidov & Vladimir Trainin & Alexander Zaitsev, 2005. "On collective non-gaussian dependence patterns in high frequency financial data," Papers physics/0506072, arXiv.org, revised Jun 2006.

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