A market resilient data-driven approach to option pricing

TL;DR

This paper introduces a data-driven ensemble method for option pricing that leverages no-arbitrage theory to improve domain adaptation and demonstrates its effectiveness through real data experiments.

Contribution

It proposes a novel ensemble approach grounded in no-arbitrage theory, enabling better domain adaptation in option pricing models.

Findings

Effective option pricing with real data

Improved domain adaptation performance

Ensemble approach outperforms traditional models

Abstract

In this paper, we present a data-driven ensemble approach for option price prediction whose derivation is based on the no-arbitrage theory of option pricing. Using the theoretical treatment, we derive a common representation space for achieving domain adaptation. The success of an implementation of this idea is shown using some real data. Then we report several experimental results for critically examining the performance of the derived pricing models.