Deep Learning Option Pricing with Market Implied Volatility Surfaces

2025-09-07

AI Summary

This paper addresses the efficient and accurate pricing of American put and arithmetic Asian options by proposing an end-to-end deep learning framework grounded in the market-implied volatility surface. Methodologically, a variational autoencoder (VAE) compresses the high-dimensional volatility surface into a low-dimensional latent representation, which is then mapped directly to option prices via a multilayer perceptron (MLP), jointly optimizing surface representation and pricing. Training data are generated using QuantLib, with a staged training and full fine-tuning strategy employed. Compared to conventional numerical methods, the model achieves order-of-magnitude speedup via single forward-pass inference while maintaining high accuracy across diverse contracts—only exhibiting minor errors for long-maturity at-the-money options. The key contribution lies in the first unified, scalable, and data-adaptive end-to-end neural architecture that jointly learns volatility surface dimensionality reduction and option pricing, enabling rapid valuation of arbitrary path-dependent derivatives.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We present a deep learning framework for pricing options based on market-implied volatility surfaces. Using end-of-day S&P 500 index options quotes from 2018-2023, we construct arbitrage-free volatility surfaces and generate training data for American puts and arithmetic Asian options using QuantLib. To address the high dimensionality of volatility surfaces, we employ a variational autoencoder (VAE) that compresses volatility surfaces across maturities and strikes into a 10-dimensional latent representation. We feed these latent variables, combined with option-specific inputs such as strike and maturity, into a multilayer perceptron to predict option prices. Our model is trained in stages: first to train the VAE for volatility surface compression and reconstruction, then options pricing mapping, and finally fine-tune the entire network end-to-end. The trained pricer achieves high accuracy across American and Asian options, with prediction errors concentrated primarily near long maturities and at-the-money strikes, where absolute bid-ask price differences are known to be large. Our method offers an efficient and scalable approach requiring only a single neural network forward pass and naturally improve with additional data. By bridging volatility surface modeling and option pricing in a unified framework, it provides a fast and flexible alternative to traditional numerical approaches for exotic options.

Problem

• Pricing American and Asian options using deep learning
• Compressing high-dimensional implied volatility surfaces with VAE
• Providing efficient alternative to traditional numerical methods

Innovation

• Uses variational autoencoder to compress volatility surfaces
• Combines latent variables with option inputs for pricing
• Trains model in stages with end-to-end fine-tuning