Computing optimal stochastic portfolio execution strategies: A parametric approach using simulations

Somayeh Moazeni, Thomas F. Coleman, Yuying Li

Research output: Contribution to journalConference articlepeer-review

Abstract

Computing optimal stochastic portfolio execution strategies under appropriate risk consideration presents great computational challenge. We investigate a parametric approach for computing optimal stochastic strategies using Monte Carlo simulations. This approach allows reduction in computational complexity by computing coefficients for a parametric representation of a stochastic dynamic strategy based on static optimization. Using this technique, constraints can be similarly handled using appropriate penalty functions. We illustrate the proposed approach to minimize the expected execution cost and Conditional Value-at-Risk (CVaR).

Original languageEnglish
Pages (from-to)342-345
Number of pages4
JournalAIP Conference Proceedings
Volume1281
DOIs
StatePublished - 2010
EventInternational Conference on Numerical Analysis and Applied Mathematics 2010, ICNAAM-2010 - Rhodes, Greece
Duration: 19 Sep 201025 Sep 2010

Keywords

  • Optimal execution
  • penalty functions
  • simulations
  • stochastic dynamic programming

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