A Neural Stochastic Volatility Model


  • Rui Luo University College London
  • Weinan Zhang Shanghai Jiao Tong University
  • Xiaojun Xu Shanghai Jiao Tong University
  • Jun Wang University College London




volatility modelling, variational inference, neural networks


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 stochvol as well as the Gaussian-process-based, on average negative log-likelihood.




How to Cite

Luo, R., Zhang, W., Xu, X., & Wang, J. (2018). A Neural Stochastic Volatility Model. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12124



AAAI Technical Track: Reasoning under Uncertainty