AUTHOR=Mücher Christian 

TITLE=Artificial Neural Network Based Non-linear Transformation of High-Frequency Returns for Volatility Forecasting

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=4

YEAR=2022

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.787534

DOI=10.3389/frai.2021.787534

ISSN=2624-8212

ABSTRACT=<p>This paper uses Long Short Term Memory Recurrent Neural Networks to extract information from the intraday high-frequency returns to forecast daily volatility. Applied to the IBM stock, we find significant improvements in the forecasting performance of models that use this extracted information compared to the forecasts of models that omit the extracted information and some of the most popular alternative models. Furthermore, we find that extracting the information through Long Short Term Memory Recurrent Neural Networks is superior to two Mixed Data Sampling alternatives.</p>