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Optimal Financial Decision Making Under Uncertainty

In: Optimal Financial Decision Making under Uncertainty

Author

Listed:
  • Giorgio Consigli

    (University of Bergamo)

  • Daniel Kuhn

    (École Polytechnique Fédérale de Lausanne)

  • Paolo Brandimarte

    (Politecnico di Torino)

Abstract

We use a fairly general framework to analyze a rich variety of financial optimization models presented in the literature, with emphasis on contributions included in this volume and a related special issue of OR Spectrum. We do not aim at providing readers with an exhaustive survey, rather we focus on a limited but significant set of modeling and methodological issues. The framework is based on a benchmark discrete-time stochastic control optimization framework, and a benchmark financial problem, asset-liability management, whose generality is considered in this chapter. A wide set of financial problems, ranging from asset allocation to financial engineering problems, is outlined, in terms of objectives, risk models, solution methods, and model users. We pay special attention to the interplay between alternative uncertainty representations and solution methods, which have an impact on the kind of solution which is obtained. Finally, we outline relevant directions for further research and optimization paradigms integration.

Suggested Citation

  • Giorgio Consigli & Daniel Kuhn & Paolo Brandimarte, 2017. "Optimal Financial Decision Making Under Uncertainty," International Series in Operations Research & Management Science, in: Giorgio Consigli & Daniel Kuhn & Paolo Brandimarte (ed.), Optimal Financial Decision Making under Uncertainty, chapter 0, pages 255-290, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-41613-7_11
    DOI: 10.1007/978-3-319-41613-7_11
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    Citations

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    Cited by:

    1. Diana Barro & Elio Canestrelli & Giorgio Consigli, 2019. "Volatility versus downside risk: performance protection in dynamic portfolio strategies," Computational Management Science, Springer, vol. 16(3), pages 433-479, July.
    2. Fu, Tianwen & Zhuang, Xinkai & Hui, Yongchang & Liu, Jia, 2017. "Convex risk measures based on generalized lower deviation and their applications," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 27-37.
    3. Stefania Corsaro & Valentina De Simone & Zelda Marino & Salvatore Scognamiglio, 2024. "Learning fused lasso parameters in portfolio selection via neural networks," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4281-4299, October.
    4. Dmitry B. Rokhlin, 2020. "Relative utility bounds for empirically optimal portfolios," Papers 2006.05204, arXiv.org.
    5. Ashrafi, Hedieh & Thiele, Aurélie C., 2021. "A study of robust portfolio optimization with European options using polyhedral uncertainty sets," Operations Research Perspectives, Elsevier, vol. 8(C).
    6. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).
    7. Dmitry B. Rokhlin, 2021. "Relative utility bounds for empirically optimal portfolios," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 93(3), pages 437-462, June.

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