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

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

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 compa...

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Bibliographic Details
Main Author: Christian Mücher
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-02-01
Series:Frontiers in Artificial Intelligence
Subjects:
neural networks
forecasting
high-frequency data
realized volatility
mixed data sampling
long short term memory
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2021.787534/full

Internet

https://www.frontiersin.org/articles/10.3389/frai.2021.787534/full

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