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

• URL: http://arxiv.org/abs/2510.24074v1
• Date: Tue, 28 Oct 2025 05:21:55 GMT
• Title: Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework
• Authors: Arman Zadgar, Somayeh Fallah, Farshid Mehrdoust,
• Abstract summary: Heston volatility model is a widely used tool in financial mathematics for pricing European options.<n>This paper introduces a hybrid deep learning-based framework that enhances both the computational efficiency and the accuracy of the calibration procedure.<n> Experimental results on real S&P 500 option data demonstrate that the deep learning approach outperforms traditional calibration techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• How to Set the Learning Rate for Large-Scale Pre-training? [73.03133634525635]
  We formalize this investigation into two distinct research paradigms: Fitting and Transfer.<n>Within the Fitting Paradigm, we introduce a Scaling Law for search factor, effectively reducing the search complexity from O(n3) to O(n*C_D*C_) via predictive modeling.<n>We extend the principles of $$Transfer to the Mixture of Experts (MoE) architecture, broadening its applicability to encompass model depth, weight decay, and token horizons.
  arXiv  Detail & Related papers  (2026-01-08T15:55:13Z)
• Sample Margin-Aware Recalibration of Temperature Scaling [20.87493013833571]
  Recent advances in deep learning have significantly improved predictive accuracy.<n>Modern neural networks remain systematically overconfident, posing risks for deployment in safety-critical scenarios.<n>We propose a lightweight, data-efficient recalibration method that precisely scales logits based on the margin between the top two logits.
  arXiv  Detail & Related papers  (2025-06-30T03:35:05Z)
• Pricing American Options using Machine Learning Algorithms [0.0]
  This study investigates the application of machine learning algorithms to pricing American options using Monte Carlo simulations. Traditional models, such as the Black-Scholes-Merton framework, often fail to adequately address the complexities of American options. By leveraging Monte Carlo methods in conjunction with Least Square Method machine learning was used.
  arXiv  Detail & Related papers  (2024-09-05T02:52:11Z)
• Sharp Calibrated Gaussian Processes [58.94710279601622]
  State-of-the-art approaches for designing calibrated models rely on inflating the Gaussian process posterior variance. We present a calibration approach that generates predictive quantiles using a computation inspired by the vanilla Gaussian process posterior variance. Our approach is shown to yield a calibrated model under reasonable assumptions.
  arXiv  Detail & Related papers  (2023-02-23T12:17:36Z)
• On Calibrating Semantic Segmentation Models: Analyses and An Algorithm [51.85289816613351]
  We study the problem of semantic segmentation calibration. Model capacity, crop size, multi-scale testing, and prediction correctness have impact on calibration. We propose a simple, unifying, and effective approach, namely selective scaling.
  arXiv  Detail & Related papers  (2022-12-22T22:05:16Z)
• On the Importance of Calibration in Semi-supervised Learning [13.859032326378188]
  State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been highly successful in leveraging a mix of labeled and unlabeled data. We introduce a family of new SSL models that optimize for calibration and demonstrate their effectiveness across standard vision benchmarks.
  arXiv  Detail & Related papers  (2022-10-10T15:41:44Z)
• On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency [47.90235939359225]
  We propose a novel mixup strategy for pre-trained language models that improves model calibration further. Our method achieves the lowest expected calibration error compared to strong baselines on both in-domain and out-of-domain test samples.
  arXiv  Detail & Related papers  (2022-03-14T23:45:08Z)
• Deep Calibration of Interest Rates Model [0.0]
  Despite the growing use of Deep Learning, classic rate models such as CIR and the Gaussian family are still widely used. In this paper, we propose to calibrate the five parameters of the G2++ model using Neural Networks.
  arXiv  Detail & Related papers  (2021-10-28T14:08:45Z)
• Predictive machine learning for prescriptive applications: a coupled training-validating approach [77.34726150561087]
  We propose a new method for training predictive machine learning models for prescriptive applications. This approach is based on tweaking the validation step in the standard training-validating-testing scheme. Several experiments with synthetic data demonstrate promising results in reducing the prescription costs in both deterministic and real models.
  arXiv  Detail & Related papers  (2021-10-22T15:03:20Z)
• Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error [46.12703434199988]
  We introduce a new differentiable surrogate for expected calibration error (DECE) that allows calibration quality to be directly optimised. We also propose a meta-learning framework that uses DECE to optimise for validation set calibration.
  arXiv  Detail & Related papers  (2021-06-17T15:47:50Z)
• On the Dark Side of Calibration for Modern Neural Networks [65.83956184145477]
  We show the breakdown of expected calibration error (ECE) into predicted confidence and refinement. We highlight that regularisation based calibration only focuses on naively reducing a model's confidence. We find that many calibration approaches with the likes of label smoothing, mixup etc. lower the utility of a DNN by degrading its refinement.
  arXiv  Detail & Related papers  (2021-06-17T11:04:14Z)
• Post-hoc Calibration of Neural Networks by g-Layers [51.42640515410253]
  In recent years, there is a surge of research on neural network calibration. It is known that minimizing Negative Log-Likelihood (NLL) will lead to a calibrated network on the training set if the global optimum is attained. We prove that even though the base network ($f$) does not lead to the global optimum of NLL, by adding additional layers ($g$) and minimizing NLL by optimizing the parameters of $g$ one can obtain a calibrated network.
  arXiv  Detail & Related papers  (2020-06-23T07:55:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.