计量经济学
波动性(金融)
股票市场指数
计算机科学
随机波动
股票市场
库存(枪支)
索引(排版)
经济
金融经济学
工程类
机械工程
生物
万维网
古生物学
马
作者
Trang Nguyen,Minh‐Ngoc Tran,David Gunawan,Robert Kohn
标识
DOI:10.1080/07350015.2022.2028631
摘要
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 nontrivial 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, for example, nonlinearity 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 U.S. stock market index SP500 and the Canada market index TSX250. An user-friendly software package together with the examples reported in the article are available at https://github.com/vbayeslab.
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