Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/268951 
Authors: 
Year of Publication: 
2023
Series/Report no.: 
FAU Discussion Papers in Economics No. 03/2023
Publisher: 
Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics, Nürnberg
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.
Subjects: 
Realized volatility
temporal fusion transformer
long short-term memory network
random forest
JEL: 
C45
C53
C58
E44
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.