A novel perspective on forecasting non-ferrous metals’ volatility: Integrating deep learning techniques with econometric models

Qi Shu, Heng Xiong, Wenjun Jiang and Rogemar Mamon

Finance Research Letters, 2023, vol. 58, issue PC

Abstract: This study puts forward a new perspective on non-ferrous metals’ volatility prediction in the futures market. Two hybrid deep learning architectures are constructed by embedding assorted convolutional neural networks (CNN) into long short-term memory (LSTM) models, and combining the LSTM networks with various generalized autoregressive conditional heteroscedasticity (GARCH)-type models. We illustrate the numerical implementation of all proposed models on four non-ferrous metal indices. Our findings suggest that the GARCH-LSTM model outperforms other alternatives by examining diverse error metrics. This study marks a significant advancement in the application of integrated deep learning models to enhance the prediction performance of commodity volatility.

Keywords: Volatility; Nonferrous metals; GARCH; Deep learning; Commodity (search for similar items in EconPapers)
JEL-codes: C53 C88 G10 G17 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323008541

DOI: 10.1016/j.frl.2023.104482

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