# On the universality of the volatility formation process: when machine learning and rough volatility agree

arXiv: https://arxiv.org/abs/2206.14114 · HTML: https://mlsanity.com/paper/2206.14114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 28 Jun 2022.

## Builds on (most cited first, 2 of 2)

- [Universal features of price formation in financial markets: perspectives from Deep Learning](https://mlsanity.com/paper/1803.06917/) (2018)
- [Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data](https://mlsanity.com/paper/2107.10941/) (2021)

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

- [Recurrent Neural Networks with more flexible memory: better predictions than rough volatility](https://mlsanity.com/paper/2308.08550/) (2023)

## Abstract (arXiv)

We train an LSTM network based on a pooled dataset made of hundreds of liquid stocks aiming to forecast the next daily realized volatility for all stocks. Showing the consistent outperformance of this universal LSTM relative to other asset-specific parametric models, we uncover nonparametric evidences of a universal volatility formation mechanism across assets relating past market realizations, including daily returns and volatilities, to current volatilities. A parsimonious parametric forecasting device combining the rough fractional stochastic volatility and quadratic rough Heston models with fixed parameters results in the same level of performance as the universal LSTM, which confirms the universality of the volatility formation process from a parametric perspective.