From Black-Scholes to Online Learning: Dynamic Hedging under Adversarial Environments
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.
Cite
@article{arxiv.1406.6084,
title = {From Black-Scholes to Online Learning: Dynamic Hedging under Adversarial Environments},
author = {Henry Lam and Zhenming Liu},
journal= {arXiv preprint arXiv:1406.6084},
year = {2014}
}