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Variational Heteroscedastic Volatility Model

arXiv 2204.05806 ↗First posted 11 Apr 2022q-fin.ST cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 16

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4 more
  1. 2019Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodologycited by 18
  2. 2017A Neural Stochastic Volatility Modelcited by 12
  3. 2013Dynamic Covariance Models for Multivariate Financial Time Seriescited by 9
  4. 2022Neural Generalised AutoRegressive Conditional Heteroskedasticitycited by 1

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