Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning

TL;DR

This paper introduces three supervised machine learning methods for pricing European call options that outperform traditional models and generalize across multiple assets without relying on volatility inputs.

Contribution

It presents novel data-driven approaches that provide a range of fair prices, improve generalization across assets, and do not depend on volatility measures.

Findings

Models outperform Black-Scholes in experiments.

Scale-free input enables multi-asset training.

Effective in pre-2020 stock market crash period.

Abstract

We propose three different data-driven approaches for pricing European-style call options using supervised machine-learning algorithms. These approaches yield models that give a range of fair prices instead of a single price point. The performance of the models are tested on two stock market indices: NIFTY$50$ and BANKNIFTY from the Indian equity market. Although neither historical nor implied volatility is used as an input, the results show that the trained models have been able to capture the option pricing mechanism better than or similar to the Black-Scholes formula for all the experiments. Our choice of scale free I/O allows us to train models using combined data of multiple different assets from a financial market. This not only allows the models to achieve far better generalization and predictive capability, but also solves the problem of paucity of data, the primary limitation of using machine learning techniques. We also illustrate the performance of the trained models in the period leading up to the 2020 Stock Market Crash (Jan 2019 to April 2020).