From Black-Scholes to Online Learning: Dynamic Hedging under Adversarial Environments

TL;DR

This paper introduces an online learning framework for option pricing in adversarial markets, establishing connections to Black-Scholes and developing algorithms for convex and non-convex options with convergence guarantees.

Contribution

It bridges adversarial online learning with classical stochastic option pricing models, providing new algorithms and theoretical insights for non-stochastic market environments.

Findings

Analogous structure to Black-Scholes for convex options

Efficient algorithms for non-convex options

Convergence results and jump extensions

Abstract

We consider a non-stochastic online learning approach to price financial options by modeling the market dynamic as a repeated game between the nature (adversary) and the investor. We demonstrate that such framework yields analogous structure as the Black-Scholes model, the widely popular option pricing model in stochastic finance, for both European and American options with convex payoffs. In the case of non-convex options, we construct approximate pricing algorithms, and demonstrate that their efficiency can be analyzed through the introduction of an artificial probability measure, in parallel to the so-called risk-neutral measure in the finance literature, even though our framework is completely adversarial. Continuous-time convergence results and extensions to incorporate price jumps are also presented.