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Correlation extrapolated

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  • Maugis, Pierre-André G.

Abstract

We provide consistent and asymptotic normal estimators of correlation that corrects the bias induced by partial samples. Through examples, we show that the estimators behave well in small-sample and yields powerful methodologies for non-linear regression as well as dependence testing.

Suggested Citation

  • Maugis, Pierre-André G., 2019. "Correlation extrapolated," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 89-95.
  • Handle: RePEc:eee:stapro:v:145:y:2019:i:c:p:89-95
    DOI: 10.1016/j.spl.2018.08.007
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    References listed on IDEAS

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    1. Michael Kalkbrener & Natalie Packham, 2015. "Correlation Under Stress In Normal Variance Mixture Models," Mathematical Finance, Wiley Blackwell, vol. 25(2), pages 426-456, April.
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    4. Dominique Guegan & Sophie A. Ladoucette, 2002. "What is the best approach to measure the interdependence between different markets," Post-Print halshs-00201333, HAL.
    5. Kristin J. Forbes & Roberto Rigobon, 2002. "No Contagion, Only Interdependence: Measuring Stock Market Comovements," Journal of Finance, American Finance Association, vol. 57(5), pages 2223-2261, October.
    6. Brian H. Boyer & Michael S. Gibson & Mico Loretan, 1997. "Pitfalls in tests for changes in correlations," International Finance Discussion Papers 597, Board of Governors of the Federal Reserve System (U.S.).
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