Annals of Operations Research

, Volume 237, Issue 1–2, pp 99–120 | Cite as

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



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.


Optimal execution Computational stochastic programming Dynamic programming Penalty functions 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Somayeh Moazeni
    • 1
  • Thomas F. Coleman
    • 2
  • Yuying Li
    • 3
  1. 1.Department of Operations Research and Financial EngineeringPrinceton UniversityPrincetonUSA
  2. 2.Department of Combinatorics and OptimizationUniversity of WaterlooWaterlooCanada
  3. 3.David R. Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada

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