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Smoothing and parametric rules for stochastic mean-CVaR optimal execution strategy

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  • Somayeh Moazeni
  • Thomas Coleman
  • Yuying Li

Abstract

Computing optimal stochastic portfolio execution strategies under an appropriate risk consideration presents many computational challenges. Using Monte Carlo simulations, we investigate an approach based on smoothing and parametric rules to minimize mean and Conditional Value-at-Risk (CVaR) of the execution cost. The proposed approach reduces computational complexity by smoothing the nondifferentiability arising from the simulation discretization and by employing a parametric representation of a stochastic strategy. We further handle constraints using a smoothed exact penalty function. Using the downside risk as an example, we show that the proposed approach can be generalized to other risk measures. In addition, we computationally illustrate the effect of including risk on the stochastic optimal execution strategy. Copyright Springer Science+Business Media New York 2016

Suggested Citation

  • Somayeh Moazeni & Thomas Coleman & Yuying Li, 2016. "Smoothing and parametric rules for stochastic mean-CVaR optimal execution strategy," Annals of Operations Research, Springer, vol. 237(1), pages 99-120, February.
    • Handle: RePEc:spr:annopr:v:237:y:2016:i:1:p:99-120:10.1007/s10479-013-1391-7
    DOI: 10.1007/s10479-013-1391-7
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    References listed on IDEAS

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    Cited by:

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    3. Somayeh Moazeni & Warren B. Powell & Boris Defourny & Belgacem Bouzaiene-Ayari, 2017. "Parallel Nonstationary Direct Policy Search for Risk-Averse Stochastic Optimization," INFORMS Journal on Computing, INFORMS, vol. 29(2), pages 332-349, May.
    4. Wei Chen & Yuxi Gai & Pankaj Gupta, 2018. "Efficiency evaluation of fuzzy portfolio in different risk measures via DEA," Annals of Operations Research, Springer, vol. 269(1), pages 103-127, October.

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