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Notes on Cumulative Entropy as a Risk Measure

Author

Listed:
  • Tahmasebi Saeid

    (Department of Statistics, Persian Gulf University, Bushehr, Iran)

  • Parsa Hojat

    (Department of Economics, Persian Gulf University, Bushehr, Iran)

Abstract

Di Crescenzo and Longobardi [Di Crescenzo and Longobardi, On cumulative entropies, J. Statist. Plann. Inference 139 2009, 12, 4072–4087] proposed the cumulative entropy (CE) as an alternative to the differential entropy. They presented an estimator of CE using empirical approach. In this paper, we consider a risk measure based on CE and compare it with the standard deviation and the Gini mean difference for some distributions. We also make empirical comparisons of these measures using samples from stock market in members of the Organization for Economic Co-operation and Development (OECD) countries.

Suggested Citation

  • Tahmasebi Saeid & Parsa Hojat, 2019. "Notes on Cumulative Entropy as a Risk Measure," Stochastics and Quality Control, De Gruyter, vol. 34(1), pages 1-7, June.
    • Handle: RePEc:bpj:ecqcon:v:34:y:2019:i:1:p:1-7:n:1
    DOI: 10.1515/eqc-2018-0019
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    References listed on IDEAS

    as
    1. Georgios Psarrakos & Jorge Navarro, 2013. "Generalized cumulative residual entropy and record values," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(5), pages 623-640, July.
    2. Davidson, Russell, 2009. "Reliable inference for the Gini index," Journal of Econometrics, Elsevier, vol. 150(1), pages 30-40, May.
    3. Jorge Navarro & Georgios Psarrakos, 2017. "Characterizations based on generalized cumulative residual entropy functions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(3), pages 1247-1260, February.
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