DeepVol: volatility forecasting from high-frequency data with dilated causal convolutions
Author:
Affiliation:
1. Oxford-Man Institute of Quantitative Finance, University of Oxford, Oxford, UK
2. Signal Processing and Learning Group, Universidad Carlos III de Madrid, Madrid, Spain
3. Machine Learning Research Group, University of Oxford, Oxford, UK
Funder
Spanish government
Comunidad de Madrid
European Union
European Research Council (ERC) through the European Union's Horizon 2020
FEDER
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/14697688.2024.2387222
Reference106 articles.
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2. Heterogeneous information arrivals and return volatility dynamics: Uncovering the long-run in high frequency returns;Andersen T.G.;J. Finance,1997
3. Answering the skeptics: Yes, standard volatility models do provide accurate forecasts;Andersen T.G.;Int. Econ. Rev. (Philadelphia),1998
4. The distribution of realized stock return volatility;Andersen T.G.;J. Financ. Econ.,2001
5. Modeling and forecasting realized volatility;Andersen T.G.;Econometrica,2003
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