3 citations · 5 across the 3 of their papers we have counts for
4 papers
Variational Heteroscedastic Volatility Model
Zexuan Yin, Paolo Barucca
We propose Variational Heteroscedastic Volatility Model (VHVM) -- an end-to-end neural network architecture capable of modelling heteroscedastic behaviour in multivariate financial…
Deep Recurrent Modelling of Granger Causality with Latent Confounding
Zexuan Yin, Paolo Barucca
Inferring causal relationships in observational time series data is an important task when interventions cannot be performed. Granger causality is a popular framework to infer pote…
Neural Generalised AutoRegressive Conditional Heteroskedasticity
Zexuan Yin, Paolo Barucca
We propose Neural GARCH, a class of methods to model conditional heteroskedasticity in financial time series. Neural GARCH is a neural network adaptation of the GARCH 1,1 model in…
Stochastic Recurrent Neural Network for Multistep Time Series Forecasting
Zexuan Yin, Paolo Barucca
Time series forecasting based on deep architectures has been gaining popularity in recent years due to their ability to model complex non-linear temporal dynamics. The recurrent ne…