Forecasting realized volatility in turbulent times using temporal fusion transformers

Johannes Frank

No 03/2023, FAU Discussion Papers in Economics from Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics

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 (search for similar items in EconPapers)
JEL-codes: C45 C53 C58 E44 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ets, nep-fmk and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:iwqwdp:032023

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