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Robust scenario-based value-at-risk optimization

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  • Oleksandr Romanko
  • Helmut Mausser

Abstract

This paper develops and tests a heuristic algorithm for scenario-based value-at-risk (VaR) optimization. Due to the high computational complexity of VaR optimization, conditional value-at-risk-based proxies are utilized for VaR objectives. It is shown that our heuristic algorithm obtains robust results with low computational complexity. Copyright IBM Corp. 2016

Suggested Citation

  • Oleksandr Romanko & Helmut Mausser, 2016. "Robust scenario-based value-at-risk optimization," Annals of Operations Research, Springer, vol. 237(1), pages 203-218, February.
    • Handle: RePEc:spr:annopr:v:237:y:2016:i:1:p:203-218:10.1007/s10479-015-1822-8
    DOI: 10.1007/s10479-015-1822-8
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    References listed on IDEAS

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    Cited by:

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    2. Yuehuan He & Oleksandr Romanko & Alina Sienkiewicz & Robert Seidman & Roy Kwon, 2021. "Cognitive User Interface for Portfolio Optimization," JRFM, MDPI, vol. 14(4), pages 1-15, April.
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    5. Dai, Zhifeng & Wang, Fei, 2019. "Sparse and robust mean–variance portfolio optimization problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1371-1378.

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