# A Statistical Recurrent Stochastic Volatility Model for Stock Markets

arXiv: https://arxiv.org/abs/1906.02884 · HTML: https://mlsanity.com/paper/1906.02884/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 7 of 7)

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- [Generalized Autoregressive Score Trees and Forests](https://mlsanity.com/paper/2305.18991/) (2023)
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- [From Deep Filtering to Deep Econometrics](https://mlsanity.com/paper/2311.06256/) (2023)
- [Loss-based Bayesian Sequential Prediction of Value at Risk with a Long-Memory and Non-linear Realized Volatility Model](https://mlsanity.com/paper/2408.13588/) (2024)

## Abstract (arXiv)

The Stochastic Volatility (SV) model and its variants are widely used in the financial sector while recurrent neural network (RNN) models are successfully used in many large-scale industrial applications of Deep Learning. Our article combines these two methods in a non-trivial way and proposes a model, which we call the Statistical Recurrent Stochastic Volatility (SR-SV) model, to capture the dynamics of stochastic volatility. The proposed model is able to capture complex volatility effects (e.g., non-linearity and long-memory auto-dependence) overlooked by the conventional SV models, is statistically interpretable and has an impressive out-of-sample forecast performance. These properties are carefully discussed and illustrated through extensive simulation studies and applications to five international stock index datasets: The German stock index DAX30, the Hong Kong stock index HSI50, the France market index CAC40, the US stock market index SP500 and the Canada market index TSX250. An user-friendly software package together with the examples reported in the paper are available at urlhttps://github.com/vbayeslab.