# A Statistical Recurrent Stochastic Volatility Model for Stock Markets

## TL;DR

This paper introduces the SR-SV model, combining stochastic volatility models with recurrent neural networks to better capture complex volatility dynamics in stock markets, demonstrating superior forecasting performance.

## Contribution

It presents a novel statistical recurrent stochastic volatility model that captures non-linearity and long-memory effects, improving volatility forecasting in financial markets.

## Key findings

- Model captures complex volatility effects like non-linearity and long-memory.
- Demonstrates superior out-of-sample forecast performance.
- Validated on five international stock index datasets.

## Abstract

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 \url{https://github.com/vbayeslab}.