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

Mücher, Christian

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

Dateien

Muecher_2-1bnro7q4n9p1y6.pdfGröße: 867.46 KBDownloads: 86

Datum

2021

Open Access-Veröffentlichung

Open Access Gold

Sammlungen

Wirtschaftswissenschaften

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Frontiers in Artificial Intelligence. Frontiers Research Foundation. 2021, 4, 787534. eISSN 2624-8212. Available under: doi: 10.3389/frai.2021.787534

Zusammenfassung

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.

Fachgebiet (DDC)

330 Wirtschaft

Schlagwörter

neural networks, forecasting, high-frequency data, realized volatility, mixed data sampling, long short term memory

Zitieren

ISO 690

MÜCHER, Christian, 2021. Artificial Neural Network Based Non-linear Transformation of High-Frequency Returns for Volatility Forecasting. In: Frontiers in Artificial Intelligence. Frontiers Research Foundation. 2021, 4, 787534. eISSN 2624-8212. Available under: doi: 10.3389/frai.2021.787534

BibTex

@article{Mucher2021Artif-57256,
  year={2021},
  doi={10.3389/frai.2021.787534},
  title={Artificial Neural Network Based Non-linear Transformation of High-Frequency Returns for Volatility Forecasting},
  volume={4},
  journal={Frontiers in Artificial Intelligence},
  author={Mücher, Christian},
  note={Article Number: 787534}
}

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