Meta-LSTR: Meta-Learning with Long Short-Term Transformer for futures volatility prediction

Yunzhu Chen, Neng Ye, Wenyu Zhang, Jiaqi Fan, Shahid Mumtaz, Xiangming Li*

*此作品的通讯作者

科研成果: 期刊稿件文献综述同行评审

摘要

Futures are essential instruments in financial markets. Accurately predicting futures volatility is crucial for calculating value-at-risk and comprehensively assessing financial uncertainty. However, the rapid changes in the futures market, the continuous emergence of new commodities, and the close interaction with spot markets create a complex market environment. This results in futures data having intricate characteristics of limited historical data, non-stationary, and non-linear, posing significant challenges for accurately predicting volatility. We propose a futures volatility prediction framework, Meta-Learning with Long Short-Term Transformer (Meta-LSTR) to tackle these challenges. To improve the understanding of market dynamics, we construct a Long-Short Term Transformer network. In conjunction with a de-stationary module and market-side information, the network can effectively capture multi-scale non-stationary features and non-linear temporal dependencies. To enhance the efficiency of limited data utilization, we employ a meta-learning approach to extract common knowledge across different varieties of futures. Comprehensive experiments using Chinese market data highlight the effectiveness of the Meta-LSTR model in futures volatility prediction. Compared to other state-of-the-art methods, the proposed Meta-LSTR model reduces prediction error by over 21.99%.

源语言英语
文章编号125926
期刊Expert Systems with Applications
265
DOI
出版状态已出版 - 15 3月 2025

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引用此

Chen, Y., Ye, N., Zhang, W., Fan, J., Mumtaz, S., & Li, X. (2025). Meta-LSTR: Meta-Learning with Long Short-Term Transformer for futures volatility prediction. Expert Systems with Applications, 265, 文章 125926. https://doi.org/10.1016/j.eswa.2024.125926