Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework

TL;DR

This paper presents a hybrid deep learning framework that significantly improves the efficiency and accuracy of calibrating the Heston model for option pricing, enabling real-time applications.

Contribution

It introduces two neural networks, PAN and CCN, to approximate option prices and correct calibration errors, enhancing traditional calibration methods.

Findings

Outperforms traditional calibration in speed and accuracy.

Demonstrates robustness on real market data.

Achieves better generalization in out-of-sample tests.

Abstract

The Heston stochastic volatility model is a widely used tool in financial mathematics for pricing European options. However, its calibration remains computationally intensive and sensitive to local minima due to the model's nonlinear structure and high-dimensional parameter space. This paper introduces a hybrid deep learning-based framework that enhances both the computational efficiency and the accuracy of the calibration procedure. The proposed approach integrates two supervised feedforward neural networks: the Price Approximator Network (PAN), which approximates the option price surface based on strike and moneyness inputs, and the Calibration Correction Network (CCN), which refines the Heston model's output by correcting systematic pricing errors. Experimental results on real S\&P 500 option data demonstrate that the deep learning approach outperforms traditional calibration techniques across multiple error metrics, achieving faster convergence and superior generalization in both in-sample and out-of-sample settings. This framework offers a practical and robust solution for real-time financial model calibration.