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Modeling stylized facts for financial time series

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

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

  • M. I. Krivoruchenko & E. Alessio & V. Frappietro & L. J. Streckert, 2004. "Modeling stylized facts for financial time series," Papers cond-mat/0401009, arXiv.org, revised Nov 2004.
    • Handle: RePEc:arx:papers:cond-mat/0401009
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    Cited by:

    1. Kovačić, Zlatko, 2007. "Forecasting volatility: Evidence from the Macedonian stock exchange," MPRA Paper 5319, University Library of Munich, Germany.
    2. 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.

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