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Numerical computation of convex risk measures

Author

Listed:
  • G. I. Papayiannis

    (Athens University of Economics and Business Athens)

  • A. N. Yannacopoulos

    (Athens University of Economics and Business Athens)

Abstract

In this work we consider the problem of numerical computation of convex risk measures, using a regularization scheme to account for undesirable fluctuations in the available historical data, combined with techniques from the Calculus of Variations.

Suggested Citation

  • G. I. Papayiannis & A. N. Yannacopoulos, 2018. "Numerical computation of convex risk measures," Annals of Operations Research, Springer, vol. 260(1), pages 417-435, January.
    • Handle: RePEc:spr:annopr:v:260:y:2018:i:1:d:10.1007_s10479-016-2284-3
    DOI: 10.1007/s10479-016-2284-3
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    References listed on IDEAS

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    1. Breuer, Thomas & Csiszár, Imre, 2013. "Systematic stress tests with entropic plausibility constraints," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1552-1559.
    2. A. Ahmadi-Javid, 2012. "Entropic Value-at-Risk: A New Coherent Risk Measure," Journal of Optimization Theory and Applications, Springer, vol. 155(3), pages 1105-1123, December.
    3. Hans Föllmer & Alexander Schied, 2002. "Convex measures of risk and trading constraints," Finance and Stochastics, Springer, vol. 6(4), pages 429-447.
    4. Wang, Shaun S. & Young, Virginia R. & Panjer, Harry H., 1997. "Axiomatic characterization of insurance prices," Insurance: Mathematics and Economics, Elsevier, vol. 21(2), pages 173-183, November.
    5. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
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