| By: |
Frank, Johannes |
| Abstract: |
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. |
| Keywords: |
Realized volatility, temporal fusion transformer, long short-term memory network, random forest |
| JEL: |
C45 C53 C58 E44 |
| Date: |
2023 |
| URL: |
http://d.repec.org/n?u=RePEc:zbw:iwqwdp:032023&r=for |