Frank J (2023)
Publication Language: English
Publication Type: Other publication type
Publication year: 2023
This paper analyzes the performance of temporal fusion transformers in forecasting
realized volatilities of stocks listed in the S&P 500 in volatile periods by comparing
the predictions with those of state-of-the-art machine learning methods as well as
GARCH models. The models are trained on weekly and monthly data based on three
different feature sets using varying training approaches including pooling methods. I
find that temporal fusion transformers show very good results in predicting financial
volatility and outperform long short-term memory networks and random forests
when using pooling methods. The use of sectoral pooling substantially improves
the predictive performance of all machine learning approaches used. The results are
robust to different ways of training the models.
APA:
Frank, J. (2023). Forecasting Realized Volatility in Turbulent Times using Temporal Fusion Transformers.
MLA:
Frank, Johannes. Forecasting Realized Volatility in Turbulent Times using Temporal Fusion Transformers. 2023.
BibTeX: Download