# A Neural Stochastic Volatility Model

arXiv: https://arxiv.org/abs/1712.00504 · HTML: https://mlsanity.com/paper/1712.00504/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2017.

## Built on by (most cited first, 12 of 12)

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- [Enhancing Profitability and Investor Confidence through Interpretable AI Models for Investment Decisions](https://mlsanity.com/paper/2312.16223/) (2023)
- [RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction](https://mlsanity.com/paper/2403.02500/) (2024)
- [ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting](https://mlsanity.com/paper/2606.02117/) (2026)
- [Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2608.11114/) (2026)

## Abstract (arXiv)

In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the joint distribution of the stochastic volatility process; the inference network approximates the conditional distribution of the latent variables given the observables. Our focus here is on the formulation of temporal dynamics of volatility over time under a stochastic recurrent neural network framework. Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms mainstream methods, e.g., deterministic models such as GARCH and its variants, and stochastic models namely the MCMC-based model stochvol as well as the Gaussian process volatility model GPVol, on average negative log-likelihood.