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Constructing Analytically Tractable Ensembles of Non-Stationary Covariances with an Application to Financial Data

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  • Frederik Meudt
  • Martin Theissen
  • Rudi Schafer
  • Thomas Guhr

Abstract

In complex systems, crucial parameters are often subject to unpredictable changes in time. Climate, biological evolution and networks provide numerous examples for such non-stationarities. In many cases, improved statistical models are urgently called for. In a general setting, we study systems of correlated quantities to which we refer as amplitudes. We are interested in the case of non-stationarity, i.e., seemingly random covariances. We present a general method to derive the distribution of the covariances from the distribution of the amplitudes. To ensure analytical tractability, we construct a properly deformed Wishart ensemble of random matrices. We apply our method to financial returns where the wealth of data allows us to carry out statistically significant tests. The ensemble that we find is characterized by an algebraic distribution which improves the understanding of large events.

Suggested Citation

  • Frederik Meudt & Martin Theissen & Rudi Schafer & Thomas Guhr, 2015. "Constructing Analytically Tractable Ensembles of Non-Stationary Covariances with an Application to Financial Data," Papers 1503.01584, arXiv.org, revised Jul 2015.
    • Handle: RePEc:arx:papers:1503.01584
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    Cited by:

    1. Andreas Muhlbacher & Thomas Guhr, 2018. "Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations," Papers 1803.00261, arXiv.org.
    2. 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.
    3. Hirdesh K. Pharasi & Suchetana Sadhukhan & Parisa Majari & Anirban Chakraborti & Thomas H. Seligman, 2021. "Dynamics of the market states in the space of correlation matrices with applications to financial markets," Papers 2107.05663, arXiv.org.
    4. Andreas Mühlbacher & Thomas Guhr, 2018. "Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations," Risks, MDPI, vol. 6(2), pages 1-25, April.

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