Recurrent latent variable conditional heteroscedasticity

Generalized autoregressive conditional heteroscedasticity (GARCH) models have long been considered as one of the most successful families of approaches for volatility modeling in financial return signals. However, this family of methods employ quite rigid assumptions regarding the evolution of the variance. In this paper, we address these issues by introducing a recurrent latent variable model, capable of capturing highly flexible functional relationships for the variances. We derive a fast, scalable, and robust to overfitting Bayesian inference algorithm, by relying on amortized variational inference. This avoids the need to compute per-data point variational parameters, but can instead compute a set of global variational parameters valid for inference at both training and test time. We evaluate the efficacy of our approach in a number of benchmarks, and compare its performance to state-of-the-art methodologies