# Pricing options and computing implied volatilities using neural networks

## TL;DR

This paper introduces a neural network-based method to rapidly value financial options and compute implied volatilities, significantly reducing computation time compared to traditional numerical methods.

## Contribution

It presents a novel application of neural networks trained on complex financial models to accelerate option pricing and implied volatility calculations.

## Key findings

- ANN reduces computation time substantially
- Effective across multiple financial models
- Maintains high accuracy in results

## Abstract

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent's iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly.