nep-for New Economics Papers
on Forecasting
Issue of 2023‒03‒20
one paper chosen by
Rob J Hyndman
Monash University

  1. Forecasting realized volatility in turbulent times using temporal fusion transformers By Frank, Johannes

  1. 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

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