Publikation: Artificial Neural Network Based Non-linear Transformation of High-Frequency Returns for Volatility Forecasting
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2021
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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.
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330 Wirtschaft
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neural networks, forecasting, high-frequency data, realized volatility, mixed data sampling, long short term memory
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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.2021.787534BibTex
@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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