Variational Heteroscedastic Volatility Model
This work addresses the challenge of accurately predicting volatility in financial markets, which is crucial for risk management and trading strategies, but it appears incremental as it builds on existing deep learning techniques for a specific domain.
The paper tackles the problem of modeling heteroscedastic behavior in multivariate financial time series by proposing the Variational Heteroscedastic Volatility Model (VHVM), which uses a neural network architecture combining a variational autoencoder and recurrent neural network to output time-varying conditional volatilities as covariance matrices, and demonstrates its effectiveness against existing methods like GARCH and SV models on foreign currency datasets.
We propose Variational Heteroscedastic Volatility Model (VHVM) -- an end-to-end neural network architecture capable of modelling heteroscedastic behaviour in multivariate financial time series. VHVM leverages recent advances in several areas of deep learning, namely sequential modelling and representation learning, to model complex temporal dynamics between different asset returns. At its core, VHVM consists of a variational autoencoder to capture relationships between assets, and a recurrent neural network to model the time-evolution of these dependencies. The outputs of VHVM are time-varying conditional volatilities in the form of covariance matrices. We demonstrate the effectiveness of VHVM against existing methods such as Generalised AutoRegressive Conditional Heteroscedasticity (GARCH) and Stochastic Volatility (SV) models on a wide range of multivariate foreign currency (FX) datasets.