  Events such as the Financial Crisis of 2007-2008 or the COVID-19 pandemic
caused significant losses to banks and insurance entities. They also
demonstrated the importance of using accurate equity risk models and having a
risk management function able to implement effective hedging strategies. Stock
volatility forecasts play a key role in the estimation of equity risk and,
thus, in the management actions carried out by financial institutions.
Therefore, this paper has the aim of proposing more accurate stock volatility
models based on novel machine and deep learning techniques. This paper
introduces a neural network-based architecture, called Multi-Transformer.
Multi-Transformer is a variant of Transformer models, which have already been
successfully applied in the field of natural language processing. Indeed, this
paper also adapts traditional Transformer layers in order to be used in
volatility forecasting models. The empirical results obtained in this paper
suggest that the hybrid models based on Multi-Transformer and Transformer
layers are more accurate and, hence, they lead to more appropriate risk
measures than other autoregressive algorithms or hybrid models based on feed
forward layers or long short term memory cells.
