# From Deep Filtering to Deep Econometrics

arXiv: https://arxiv.org/abs/2311.06256 · HTML: https://mlsanity.com/paper/2311.06256/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 49 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [Particle Filter Networks with Application to Visual Localization](https://mlsanity.com/paper/1805.08975/) (2018)
- [Particle Filter Recurrent Neural Networks](https://mlsanity.com/paper/1905.12885/) (2019)
- [A Statistical Recurrent Stochastic Volatility Model for Stock Markets](https://mlsanity.com/paper/1906.02884/) (2019)

## Abstract (arXiv)

Calculating true volatility is an essential task for option pricing and risk management. However, it is made difficult by market microstructure noise. Particle filtering has been proposed to solve this problem as it favorable statistical properties, but relies on assumptions about underlying market dynamics. Machine learning methods have also been proposed but lack interpretability, and often lag in performance. In this paper we implement the SV-PF-RNN: a hybrid neural network and particle filter architecture. Our SV-PF-RNN is designed specifically with stochastic volatility estimation in mind. We then show that it can improve on the performance of a basic particle filter.