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

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• 作者：Somayeh Moazeni ; Thomas F. Coleman ; Yuying Li
• 关键词：Optimal execution ; Computational stochastic programming ; Dynamic programming ; Penalty functions
• 刊名：Annals of Operations Research
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：237
• 期：1-2
• 页码：99-120
• 全文大小：1,325 KB
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• 作者单位：Somayeh Moazeni (1)
  Thomas F. Coleman (2)
  Yuying Li (3)
  
  1. Department of Operations Research and Financial Engineering, Princeton University, Sherrerd Hall, Charlton Street, Princeton, New Jersey, 08544, USA
  2. Department of Combinatorics and Optimization, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, Canada
  3. David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, Canada
  
• 刊物类别：Business and Economics
• 刊物主题：Economics
  Operation Research and Decision Theory
  Combinatorics
  Theory of Computation
  
• 出版者：Springer Netherlands
• ISSN：1572-9338

文摘

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.