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Modeling cross correlations within a many-assets market.

H E Roman1, M Albergante, M Colombo

  • 1Dipartimento di Fisica, Università di Milano-Bicocca, Piazza della Scienza 3, 20126 Milan, Italy.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|April 12, 2006
PubMed
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This study introduces a market simulation model with stochastic volatility, enhancing cross-asset correlations. The improved model more accurately reflects real market behavior and price variations.

Area of Science:

  • Quantitative Finance
  • Computational Economics
  • Statistical Modeling

Background:

  • Traditional one-factor models simplify asset correlations.
  • Real market data often exhibits complex cross-correlations and fat tails.

Purpose of the Study:

  • To develop a more realistic market simulation model.
  • To capture complex cross-asset correlations and price variation distributions.
  • To improve agreement with empirical market behavior.

Main Methods:

  • Simulation of a many-assets market.
  • Application of the one-factor model for initial correlation analysis.
  • Extension of the model with autoregressive stochastic volatility.
  • Comparison with empirical data from 445 Standard and Poors 500 stocks.

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Main Results:

  • The extended model reproduces fat tails in logarithmic price variations.
  • Introduced stochastic volatility enhances cross-correlations between time series.
  • The model demonstrates improved agreement with real market cross-correlations.

Conclusions:

  • Stochastic volatility is crucial for realistic market simulations.
  • The proposed model offers a better representation of complex market dynamics.
  • This approach advances the simulation of financial markets.