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Entropy based risk measures

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  • Pichler, Alois
  • Schlotter, Ruben

Abstract

Entropy is a measure of self-information which is used to quantify information losses. Entropy was developed in thermodynamics, but is also used to compare probabilities based on their deviating information content. Corresponding model uncertainty is of particular interest and importance in stochastic programming and its applications like mathematical finance, as complete information is not accessible or manageable in general.

Suggested Citation

  • Pichler, Alois & Schlotter, Ruben, 2020. "Entropy based risk measures," European Journal of Operational Research, Elsevier, vol. 285(1), pages 223-236.
    • Handle: RePEc:eee:ejores:v:285:y:2020:i:1:p:223-236
    DOI: 10.1016/j.ejor.2019.01.016
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    Cited by:

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    2. Righi, Marcelo Brutti & Müller, Fernanda Maria & Moresco, Marlon Ruoso, 2020. "On a robust risk measurement approach for capital determination errors minimization," Insurance: Mathematics and Economics, Elsevier, vol. 95(C), pages 199-211.
    3. Cao Son Tran & Dan Nicolau & Richi Nayak & Peter Verhoeven, 2021. "Modeling Credit Risk: A Category Theory Perspective," JRFM, MDPI, vol. 14(7), pages 1-21, July.
    4. Park, Jangho & Bayraksan, Güzin, 2023. "A multistage distributionally robust optimization approach to water allocation under climate uncertainty," European Journal of Operational Research, Elsevier, vol. 306(2), pages 849-871.
    5. Jiarui Chu & Ludovic Tangpi, 2021. "Non-asymptotic estimation of risk measures using stochastic gradient Langevin dynamics," Papers 2111.12248, arXiv.org, revised Feb 2023.
    6. Zou, Zhenfeng & Wu, Qinyu & Xia, Zichao & Hu, Taizhong, 2023. "Adjusted Rényi entropic Value-at-Risk," European Journal of Operational Research, Elsevier, vol. 306(1), pages 255-268.

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