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A New Method to Estimate the Noise in Financial Correlation Matrices

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  • Thomas Guhr

    (Mathematical Physics, LTH, Lunds Universitet, Lund, Sweden)

  • Bernd Kaelber

    (MPI Kernphysik, Heidelberg, Germany)

Abstract

Financial correlation matrices measure the unsystematic correlations between stocks. Such information is important for risk management. The correlation matrices are known to be ``noise dressed''. We develop a new and alternative method to estimate this noise. To this end, we simulate certain time series and random matrices which can model financial correlations. With our approach, different correlation structures buried under this noise can be detected. Moreover, we introduce a measure for the relation between noise and correlations. Our method is based on a power mapping which efficiently suppresses the noise. Neither further data processing nor additional input is needed.

Suggested Citation

  • Thomas Guhr & Bernd Kaelber, 2002. "A New Method to Estimate the Noise in Financial Correlation Matrices," Papers cond-mat/0206577, arXiv.org.
    • Handle: RePEc:arx:papers:cond-mat/0206577
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    Cited by:

    1. Zhao, Longfeng & Wang, Gang-Jin & Wang, Mingang & Bao, Weiqi & Li, Wei & Stanley, H. Eugene, 2018. "Stock market as temporal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1104-1112.

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