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Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility
- Source :
- Mathematics, Vol 9, Iss 1794, p 1794 (2021)
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- 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.
- Subjects :
- Computer Science - Machine Learning
0209 industrial biotechnology
Computer science
General Mathematics
02 engineering and technology
Statistics - Computation
risk management
Quantitative Finance - Computational Finance
020901 industrial engineering & automation
Order (exchange)
QA1-939
0202 electrical engineering, electronic engineering, information engineering
Computer Science (miscellaneous)
Econometrics
stock volatility
Engineering (miscellaneous)
Risk management
Transformer (machine learning model)
Equity risk
Artificial neural network
business.industry
Deep learning
deep learning
neural networks
Autoregressive model
transformer
020201 artificial intelligence & image processing
Artificial intelligence
Volatility (finance)
business
Mathematics
Subjects
Details
- ISSN :
- 22277390
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- Mathematics
- Accession number :
- edsair.doi.dedup.....1fab80e598707d7277b07f05f6366038