Multivariate distribution of returns in financial time series

  • Authors:

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

  • Affiliations:

  • (Correspd. mikhail.krivoruchenko@itep.ru) Metronome-Ricerca sui Mercati Finanziari, Torino, Italy and Inst. for Theor. and Exper. Physics, Moscow, Russia and Institut für Theoretische Physik, ...;afa Metronome-Ricerca sui Mercati Finanziari, C. so Vittorio Emanuele 84, 10121 Torino, Italy;afa Metronome-Ricerca sui Mercati Finanziari, C. so Vittorio Emanuele 84, 10121 Torino, Italy;afa Metronome-Ricerca sui Mercati Finanziari, C. so Vittorio Emanuele 84, 10121 Torino, Italy

  • Venue:
  • Journal of Computational Methods in Sciences and Engineering
  • Year:
  • 2006

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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). Free parameters of the model are fixed over the long term by fitting 100+ years of daily prices of the Dow Jones 30 Industrial Average. The multivariate probability density functions which we have constructed can be used for pricing derivative securities and risk management.