# Deep Calibration of Interest Rates Model

arXiv: https://arxiv.org/abs/2110.15133 · HTML: https://mlsanity.com/paper/2110.15133/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Deep Learning in Finance](https://mlsanity.com/paper/1602.06561/) (2016)
- [A neural network-based framework for financial model calibration](https://mlsanity.com/paper/1904.10523/) (2019)

## Built on by (most cited first, 1 of 1)

- [Deep Calibration With Artificial Neural Network: A Performance Comparison on Option Pricing Models](https://mlsanity.com/paper/2303.08760/) (2023)

## Abstract (arXiv)

For any financial institution, it is essential to understand the behavior of interest rates. Despite the growing use of Deep Learning, for many reasons (expertise, ease of use, etc.), 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. Our first model is a Fully Connected Neural Network and is trained on covariances and correlations of Zero-Coupon and Forward rates. We show that covariances are more suited to the problem than correlations due to the effects of the unfeasible backpropagation phenomenon, which we analyze in this paper. The second model is a Convolutional Neural Network trained on Zero-Coupon rates with no further transformation. Our numerical tests show that our calibration based on deep learning outperforms the classic calibration method used as a benchmark. Additionally, our Deep Calibration approach is designed to be systematic. To illustrate this feature, we applied it to calibrate the popular CIR intensity model.