Gold volatility prediction using a CNN-LSTM approach

作者：

Highlights：

• Deep Learning can be applied in financial forecast, improving results of classic models.

• Proposed model with architecture based on a CNN-LSTM combination.

• Images associated to time series that have static and dynamic information of the data.

• CNN-LSTM model to predict the realized volatility for the price of gold.

• The proposed model improves the results respect a normal LSTM network and GARCH.

摘要

•Deep Learning can be applied in financial forecast, improving results of classic models.•Proposed model with architecture based on a CNN-LSTM combination.•Images associated to time series that have static and dynamic information of the data.•CNN-LSTM model to predict the realized volatility for the price of gold.•The proposed model improves the results respect a normal LSTM network and GARCH.

论文关键词：Gold price volatility,Volatility forecasting,Deep learning,CNN,LSTM,Stock returns forecasting,Hyperparameter setting

论文评审过程：Received 24 June 2019, Revised 23 December 2019, Accepted 23 April 2020, Available online 4 May 2020, Version of Record 18 May 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113481