A Duality Result for Robust Optimization with Expectation Constraints
Abstract
This paper demonstrates a practical method for computing the solution of an expectation-constrained robust maximization problem with immediate applications to model-free no-arbitrage bounds and super-replication values for many financial derivatives. While the previous literature has connected super-replication values to a convex minimization problem whose objective function is related to a sequence of iterated concave envelopes, we show how this whole process can be encoded in a single convex minimization problem. The natural finite-dimensional approximation of this minimization problem results in an easily-implementable sparse linear program. We highlight this technique by obtaining no-arbitrage bounds on the prices of forward-starting options, continuously-monitored variance swaps, and discretely-monitored gamma swaps, each subject to observed bid-ask spreads of finitely-many vanilla options.
Keywords
Cite
@article{arxiv.1610.01227,
title = {A Duality Result for Robust Optimization with Expectation Constraints},
author = {Christopher W. Miller},
journal= {arXiv preprint arXiv:1610.01227},
year = {2016}
}