Robust Hedging of path-dependent options using a min-max algorithm

TL;DR

This paper introduces a model-free, min-max optimization approach for robustly hedging path-dependent options using static portfolios of vanilla options and underlying assets, inspired by Martingale Optimal Transport theory.

Contribution

It develops a novel min-max framework for static hedging of path-dependent options that minimizes worst-case hedging error without relying on a specific model.

Findings

Numerical scheme effectively solves the min-max problem with finite vanilla option prices.

Hedging performance is validated under Black-Scholes and Merton Jump diffusion models.

Theoretical bounds on hedging error at the target option's maturity are provided.

Abstract

We consider an investor who wants to hedge a path-dependent option with maturity $T$ using a static hedging portfolio using cash, the underlying, and vanilla put/call options on the same underlying with maturity $ t_1$, where $0 < t_1 < T$. We propose a model-free approach to construct such a portfolio. The framework is inspired by the \textit{primal-dual} Martingale Optimal Transport (MOT) problem, which was pioneered by \cite{beiglbock2013model}. The optimization problem is to determine the portfolio composition that minimizes the expected worst-case hedging error at $t_1$ (that coincides with the maturity of the options that are used in the hedging portfolio). The worst-case scenario corresponds to the distribution that yields the worst possible hedging performance. This formulation leads to a \textit{min-max} problem. We provide a numerical scheme for solving this problem when a finite number of vanilla option prices are available. Numerical results on the hedging performance of this model-free approach when the option prices are generated using a \textit{Black-Scholes} and a \textit{Merton Jump diffusion} model are presented. We also provide theoretical bounds on the hedging error at $T$, the maturity of the target option.