# GARCH-Informed Neural Networks for Volatility Prediction in Financial Markets

arXiv: https://arxiv.org/abs/2410.00288 · HTML: https://mlsanity.com/paper/2410.00288/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Deep Learning for Time-Series Analysis](https://mlsanity.com/paper/1701.01887/) (2017)
- [Financial Market Prediction](https://mlsanity.com/paper/1503.02328/) (2015)

## Built on by (most cited first, 3 of 3)

- [A Survey of Financial AI: Architectures, Advances and Open Challenges](https://mlsanity.com/paper/2411.12747/) (2024)
- [Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring](https://mlsanity.com/paper/2511.23036/) (2025)
- [Latent-Regime Bias Auditing for Volatility Forecasting](https://mlsanity.com/paper/2608.01599/) (2026)

## Abstract (arXiv)

Volatility, which indicates the dispersion of returns, is a crucial measure of risk and is hence used extensively for pricing and discriminating between different financial investments. As a result, accurate volatility prediction receives extensive attention. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its succeeding variants are well established models for stock volatility forecasting. More recently, deep learning models have gained popularity in volatility prediction as they demonstrated promising accuracy in certain time series prediction tasks. Inspired by Physics-Informed Neural Networks (PINN), we constructed a new, hybrid Deep Learning model that combines the strengths of GARCH with the flexibility of a Long Short-Term Memory (LSTM) Deep Neural Network (DNN), thus capturing and forecasting market volatility more accurately than either class of models are capable of on their own. We refer to this novel model as a GARCH-Informed Neural Network (GINN). When compared to other time series models, GINN showed superior out-of-sample prediction performance in terms of the Coefficient of Determination (R2), Mean Squared Error (MSE), and Mean Absolute Error (MAE).