# A neural network-based framework for financial model calibration

arXiv: https://arxiv.org/abs/1904.10523 · HTML: https://mlsanity.com/paper/1904.10523/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error](https://mlsanity.com/paper/2106.09613/) (2021)
- [Universal randomised signatures for generative time series modelling](https://mlsanity.com/paper/2406.10214/) (2024)
- [Machine Learning in Finance-Emerging Trends and Challenges](https://mlsanity.com/paper/2110.11999/) (2021)
- [Enhancing Financial Market Predictions: Causality-Driven Feature Selection](https://mlsanity.com/paper/2408.01005/) (2024)
- [Deep Calibration of Interest Rates Model](https://mlsanity.com/paper/2110.15133/) (2021)
- [Applying Deep Learning to Calibrate Stochastic Volatility Models](https://mlsanity.com/paper/2309.07843/) (2023)
- [Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework](https://mlsanity.com/paper/2510.24074/) (2025)
- [Integrating LSTM Networks with Neural Levy Processes for Financial Forecasting](https://mlsanity.com/paper/2512.07860/) (2025)
- [Shapley-based Structural Analysis of Neural Calibration for Stochastic Volatility Models](https://mlsanity.com/paper/2610.03076/) (2026)

## Abstract (arXiv)

A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons within a machine learning framework, based on available financial option prices. The framework consists of two parts: a forward pass in which we train the weights of the ANN off-line, valuing options under many different asset model parameter settings; and a backward pass, in which we evaluate the trained ANN-solver on-line, aiming to find the weights of the neurons in the input layer. The rapid on-line learning of implied volatility by ANNs, in combination with the use of an adapted parallel global optimization method, tackles the computation bottleneck and provides a fast and reliable technique for calibrating model parameters while avoiding, as much as possible, getting stuck in local minima. Numerical experiments confirm that this machine-learning framework can be employed to calibrate parameters of high-dimensional stochastic volatility models efficiently and accurately.