A Statistical Recurrent Stochastic Volatility Model for Stock Markets
将随机波动率模型与递归神经网络结合，提出统计递归随机波动率模型，能捕捉非线性、长记忆等复杂波动效应，在五个国际股指数据上表现出色，并提供了开源软件包。
The Stochastic Volatility (SV) model and its variants are widely used in the financial sector, while recurrent neural network (RNN) models are successfully used in many large-scale industrial applications of Deep Learning. We combine these two methods in a non-trivial way and propose a model, which we call the Statistical Recurrent Stochastic Volatility (SR-SV) model, to capture the dynamics of stochastic volatility. The proposed model is able to capture complex volatility effects, e.g., non-linearity and long-memory auto-dependence, overlooked by the conventional SV models, is statistically interpretable and has an impressive out-of-sample forecast performance. These properties are carefully discussed and illustrated through extensive simulation studies and applications to five international stock index datasets: The German stock index DAX30, the Hong Kong stock index HSI50, the France market index CAC40, the US stock market index SP500 and the Canada market index TSX250. An user-friendly software package together with the examples reported in the paper are available at https://github.com/vbayeslab.