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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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Detalles Bibliográficos
Autor principal: Mücher, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8873984/
https://www.ncbi.nlm.nih.gov/pubmed/35224478
http://dx.doi.org/10.3389/frai.2021.787534