This study aims to compare multiple deep learning-based forecasters for the task of predicting volatility using multivariate data. The paper evaluates a range of models, starting from simpler and shallower ones and progressing to deeper and more complex architectures. Additionally, the performance of these models is compared against naive predictions and variations of classical GARCH models. The prediction of volatility for five assets, namely S&P500, NASDAQ100, gold, silver, and oil, is specifically addressed using GARCH models, Multi-Layer Perceptrons, Recurrent Neural Networks, Temporal Convolutional Networks, and the Temporal Fusion Transformer. In the majority of cases, the Temporal Fusion Transformer, followed by variants of the Temporal Convolutional Network, outperformed classical approaches and shallow networks. These experiments were repeated, and the differences observed between the competing models were found to be statistically significant, thus providing strong encouragement for their practical application.

翻译：本研究旨在比较多种基于深度学习的预测模型在多变量数据波动率预测任务中的性能。本文评估了一系列模型，从较为简单浅层的架构逐步深入到更复杂深层的结构。此外，将这些模型的性能与朴素预测及经典GARCH模型变体进行对比。针对S&P500、NASDAQ100、黄金、白银和石油五种资产的波动率预测，我们分别运用了GARCH模型、多层感知机、循环神经网络、时序卷积网络以及时序融合Transformer。在大多数情况下，时序融合Transformer及其后的时序卷积网络变体均优于传统方法和浅层网络。实验重复进行后发现，竞争模型之间的差异具有统计显著性，从而有力支持了它们的实际应用价值。