用深度学习提升赫斯顿模型校准速度与精度
Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework
- 双神经网络协同逼近期权价格并修正系统误差
- 实测优于传统方法，收敛更快泛化更强
- 适合高频交易与实时风险控制场景
赫斯顿随机波动率模型是金融数学中广泛使用的欧式期权定价工具，但其校准过程计算复杂且易陷入局部最优，因其非线性结构和高维参数空间。本文提出一种基于深度学习的混合框架，显著提升校准效率与精度。该方法包含两个监督式前馈神经网络：价格近似网络（PAN）根据行权价与平值度输入近似期权价格曲面；校准修正网络（CCN）则对赫斯顿模型输出进行系统性误差修正。基于真实S&P 500期权数据的实验表明，该方法在多个误差指标上均优于传统校准技术，在样本内与样本外均实现更快收敛与更优泛化能力。该框架为实时金融模型校准提供了实用且稳健的解决方案。
原文摘要 · Abstract (English)
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.
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