论文标题
变分异性波动率模型
Variational Heteroscedastic Volatility Model
论文作者
论文摘要
我们提出了变分异形波动率模型（VHVM） - 一种端到端的神经网络体系结构，能够在多元财务时间序列中对异性行为进行建模。 VHVM利用深度学习的几个领域（即顺序建模和表示学习）的最新进展来模拟不同资产回报之间的复杂时间动态。 VHVM的核心是由捕获资产之间关系的变异自动编码器和一个经常性的神经网络组成，以建模这些依赖性的时间进化。 VHVM的输出是以协方差矩阵的形式变化的条件波动。我们证明了VHVM针对现有方法的有效性，例如广泛的自动回归有条件异方差（GARCH）和随机波动率（SV）模型（SV）模型在广泛的多元外币（FX）数据集中。
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.
