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Robust CCMV model with short selling and risk-neutral interest rate

Author

Listed:
  • Khodamoradi, T.
  • Salahi, M.
  • Najafi, A.R.

Abstract

Robust optimization is a powerful tool for managing uncertainty in financial optimization problems. In this paper, we study the cardinality constraints mean–variance portfolio optimization model with short selling and risk-neutral interest rate under the return and covariance matrix uncertainty. Equivalent mixed integer second order cone programming formulation of its robust counterpart is given for the interval uncertainty. Finally, using S&P 500 index data, original and robust models are compared in terms of Sharpe ratios.

Suggested Citation

  • Khodamoradi, T. & Salahi, M. & Najafi, A.R., 2020. "Robust CCMV model with short selling and risk-neutral interest rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    • Handle: RePEc:eee:phsmap:v:547:y:2020:i:c:s0378437120301710
    DOI: 10.1016/j.physa.2020.124429
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    Cited by:

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    3. Tahereh Khodamoradi & Maziar Salahi & Ali Reza Najafi, 2021. "Cardinality-constrained portfolio optimization with short selling and risk-neutral interest rate," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 197-214, June.
    4. Zhifeng Dai & Jie Kang, 2022. "Some new efficient mean–variance portfolio selection models," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4784-4796, October.
    5. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2021. "Robust Portfolio Selection Problems: A Comprehensive Review," Papers 2103.13806, arXiv.org, revised Jan 2022.
    6. Tahereh Khodamoradi & Maziar Salahi, 2023. "Extended mean-conditional value-at-risk portfolio optimization with PADM and conditional scenario reduction technique," Computational Statistics, Springer, vol. 38(2), pages 1023-1040, June.
    7. Vrinda Dhingra & Shiv Kumar Gupta & Amita Sharma, 2023. "Norm constrained minimum variance portfolios with short selling," Computational Management Science, Springer, vol. 20(1), pages 1-35, December.

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