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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
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    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.

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    Bibliographic Info

    Paper provided by in its series Papers with number cond-mat/0401009.

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    Date of creation: Jan 2004
    Date of revision: Nov 2004
    Publication status: Published in Physica A 344, 263-266 (2004)
    Handle: RePEc:arx:papers:cond-mat/0401009

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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.


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