  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.
