# Deep Stochastic Volatility Model

arXiv: https://arxiv.org/abs/2102.12658 · HTML: https://mlsanity.com/paper/2102.12658/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 25 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [A Recurrent Latent Variable Model for Sequential Data](https://mlsanity.com/paper/1506.02216/) (2015)
- [Structured Inference Networks for Nonlinear State Space Models](https://mlsanity.com/paper/1609.09869/) (2016)
- [Sequential Neural Models with Stochastic Layers](https://mlsanity.com/paper/1605.07571/) (2016)
- [Learning Stochastic Recurrent Networks](https://mlsanity.com/paper/1411.7610/) (2014)
- [A Neural Stochastic Volatility Model](https://mlsanity.com/paper/1712.00504/) (2017)
- [Gaussian Process Volatility Model](https://mlsanity.com/paper/1402.3085/) (2014)
- [Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series](https://mlsanity.com/paper/1811.03711/) (2018)

## Built on by (most cited first, 2 of 2)

- [Estimating the Value-at-Risk by Temporal VAE](https://mlsanity.com/paper/2112.01896/) (2021)
- [DTD-VAE: Disentangled Temporal Dependencies VAE for Credit Risk Prediction](https://mlsanity.com/paper/2608.26473/) (2026)

## Abstract (arXiv)

Volatility for financial assets returns can be used to gauge the risk for financial market. We propose a deep stochastic volatility model (DSVM) based on the framework of deep latent variable models. It uses flexible deep learning models to automatically detect the dependence of the future volatility on past returns, past volatilities and the stochastic noise, and thus provides a flexible volatility model without the need to manually select features. We develop a scalable inference and learning algorithm based on variational inference. In real data analysis, the DSVM outperforms several popular alternative volatility models. In addition, the predicted volatility of the DSVM provides a more reliable risk measure that can better reflex the risk in the financial market, reaching more quickly to a higher level when the market becomes more risky and to a lower level when the market is more stable, compared with the commonly used GARCH type model with a huge data set on the U.S. stock market.