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 language | English |
---|---|
Pages (from-to) | 342-345 |
Number of pages | 4 |
Journal | AIP Conference Proceedings |
Volume | 1281 |
DOIs | |
State | Published - 2010 |
Event | International Conference on Numerical Analysis and Applied Mathematics 2010, ICNAAM-2010 - Rhodes, Greece Duration: 19 Sep 2010 → 25 Sep 2010 |
Keywords
- Optimal execution
- penalty functions
- simulations
- stochastic dynamic programming