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Non-Stationarity in Financial Time Series and Generic Features

Author

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  • Thilo A. Schmitt
  • Desislava Chetalova
  • Rudi Schafer
  • Thomas Guhr

Abstract

Financial markets are prominent examples for highly non-stationary systems. Sample averaged observables such as variances and correlation coefficients strongly depend on the time window in which they are evaluated. This implies severe limitations for approaches in the spirit of standard equilibrium statistical mechanics and thermodynamics. Nevertheless, we show that there are similar generic features which we uncover in the empirical return distributions for whole markets. We explain our findings by setting up a random matrix model.

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  • Thilo A. Schmitt & Desislava Chetalova & Rudi Schafer & Thomas Guhr, 2013. "Non-Stationarity in Financial Time Series and Generic Features," Papers 1304.5130, arXiv.org, revised May 2013.
    • Handle: RePEc:arx:papers:1304.5130
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    Cited by:

    1. Joachim Sicking & Thomas Guhr & Rudi Schafer, 2016. "Concurrent Credit Portfolio Losses," Papers 1604.06917, arXiv.org, revised Jan 2017.
    2. Pier Francesco Procacci & Tomaso Aste, 2018. "Forecasting market states," Papers 1807.05836, arXiv.org, revised May 2019.
    3. Joel Bun & Jean-Philippe Bouchaud & Marc Potters, 2016. "Cleaning large correlation matrices: tools from random matrix theory," Papers 1610.08104, arXiv.org.
    4. Desislava Chetalova & Rudi Schafer & Thomas Guhr, 2014. "Zooming into market states," Papers 1406.5386, arXiv.org.
    5. Desislava Chetalova & Marcel Wollschlager & Rudi Schafer, 2015. "Dependence structure of market states," Papers 1503.09004, arXiv.org, revised Jul 2015.
    6. Andreas Muhlbacher & Thomas Guhr, 2018. "Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations," Papers 1803.00261, arXiv.org.
    7. Andreas Muhlbacher & Thomas Guhr, 2017. "Extreme portfolio loss correlations in credit risk," Papers 1706.09809, arXiv.org.
    8. Todea, Alexandru, 2016. "Cross-correlations between volatility, volatility persistence and stock market integration: the case of emergent stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 87(C), pages 208-215.
    9. Rachid Guennouni Hassani & Alexis Gilles & Emmanuel Lassalle & Arthur D'enouveaux, 2020. "Predicting Stock Returns with Batched AROW," Papers 2003.03076, arXiv.org, revised Mar 2020.
    10. Thomas Guhr & Andreas Schell, 2020. "Exact Multivariate Amplitude Distributions for Non-Stationary Gaussian or Algebraic Fluctuations of Covariances or Correlations," Papers 2011.07570, arXiv.org.
    11. Marcel Wollschlager & Rudi Schafer, 2015. "Impact of non-stationarity on estimating and modeling empirical copulas of daily stock returns," Papers 1506.08054, arXiv.org.
    12. Rachid Guennouni Hassani & Alexis Gilles & Emmanuel Lassalle & Arthur Dénouveaux, 2020. "Predicting Stock Returns with Batched AROW," Working Papers hal-02496048, HAL.

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