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No-arbitrage bounds for financial scenarios

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  • Geyer, Alois
  • Hanke, Michael
  • Weissensteiner, Alex

Abstract

We derive no-arbitrage bounds for expected excess returns to generate scenarios used in financial applications. The bounds allow to distinguish three regions: one where arbitrage opportunities will never exist, a second where arbitrage may be present, and a third, where arbitrage opportunities will always exist. No-arbitrage bounds are derived in closed form for a given covariance matrix using the least possible number of scenarios. Empirical examples illustrate the practical potential of knowing these bounds.

Suggested Citation

  • Geyer, Alois & Hanke, Michael & Weissensteiner, Alex, 2014. "No-arbitrage bounds for financial scenarios," European Journal of Operational Research, Elsevier, vol. 236(2), pages 657-663.
    • Handle: RePEc:eee:ejores:v:236:y:2014:i:2:p:657-663
    DOI: 10.1016/j.ejor.2014.01.027
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    Cited by:

    1. Owadally, Iqbal & Jang, Chul & Clare, Andrew, 2021. "Optimal investment for a retirement plan with deferred annuities," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 51-62.
    2. Staino, Alessandro & Russo, Emilio, 2015. "A moment-matching method to generate arbitrage-free scenarios," European Journal of Operational Research, Elsevier, vol. 246(2), pages 619-630.
    3. Michele Costola & Bertrand Maillet & Zhining Yuan & Xiang Zhang, 2024. "Mean–variance efficient large portfolios: a simple machine learning heuristic technique based on the two-fund separation theorem," Annals of Operations Research, Springer, vol. 334(1), pages 133-155, March.
    4. Geyer, Alois & Hanke, Michael & Weissensteiner, Alex, 2014. "No-Arbitrage ROM simulation," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 66-79.
    5. Owadally, Iqbal & Jang, Chul & Clare, Andrew, 2021. "Optimal investment for a retirement plan with deferred annuities allowing for inflation and labour income risk," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1132-1146.
    6. Braouezec, Yann & Grunspan, Cyril, 2016. "A new elementary geometric approach to option pricing bounds in discrete time models," European Journal of Operational Research, Elsevier, vol. 249(1), pages 270-280.
    7. Chul Jang & Andrew Clare & Iqbal Owadally, 2024. "Liability-driven investment for pension funds: stochastic optimization with real assets," Risk Management, Palgrave Macmillan, vol. 26(3), pages 1-32, September.
    8. Hanke, Michael & Penev, Spiridon & Schief, Wolfgang & Weissensteiner, Alex, 2017. "Random orthogonal matrix simulation with exact means, covariances, and multivariate skewness," European Journal of Operational Research, Elsevier, vol. 263(2), pages 510-523.

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