Tuesday, 14:15 - 14:40 h, Room: H 3027


Somayeh Moazeni
Regularized robust optimization for optimal portfolio execution

Coauthors: Thomas F. Coleman, Yuying Li


An uncertainty set is a crucial component in robust optimization. Unfortunately, it is often unclear
how to specify it precisely. Thus it is important to study sensitivity of the robust solution to variations
in the uncertainty set, and to develop a method which improves stability of the robust solution. To address these issues, we focus on uncertainty in the price impact parameters in the optimalportfolio execution problem. We illustrate that a small variation in the uncertainty set may result in a large change in the robust solution. We then propose a regularized robust optimization formulation which yields a solution with a better stability property than the classical robust solution. In this approach, the
uncertainty set is regularized through a regularization constraint. The regularized robust
solution is then more stable with respect to variation in the uncertainty set specification, in addition to
being more robust to estimation errors in the price impact parameters. We show that the regularized robust solution can be computed efficiently using convex optimization. We also study implications of the regularization on the
solution and its corresponding execution cost.


Talk 3 of the invited session Tue.2.H 3027
"Financial optimization" [...]
Cluster 7
"Finance & economics" [...]


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