3 citations · 12 across the 6 of their papers we have counts for
12 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…
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…
Evaluating structural edge importance in temporal networks
Isobel Seabrook, Paolo Barucca, Fabio Caccioli
To monitor risk in temporal financial networks, we need to understand how individual behaviours affect the global evolution of networks. Here we define a structural importance metr…
Simplicial persistence of financial markets: filtering, generative processes and portfolio risk
Jeremy D. Turiel, Paolo Barucca, Tomaso Aste
We introduce simplicial persistence, a measure of time evolution of network motifs in subsequent temporal layers. We observe long memory in the evolution of structures from correla…
Image Processing Tools for Financial Time Series Classification
Bairui Du, Delmiro Fernandez-Reyes, Paolo Barucca
The application of deep learning to time series forecasting is one of the major challenges in present machine learning. We propose a novel methodology that combines machine learnin…