TensorFlow implementation of the HARNet model for realized volatility forecasting.
R. Reisenhofer, X. Bayer, and N. Hautsch
HARNet: A Convolutional Neural Network for Realized Volatility Forecasting
arXiv preprint arXiv:2205.07719, 2022
https://doi.org/10.48550/arXiv.2205.07719
Please cite the paper above when using the HARNet package in your research.
Clone the repository and use
pip install -e HARNet/to install the package.
Go to the HARNet root directory
cd HARNetan start single experiments based on one of the preset configuration files
harnet ./configs/RV/RecField_20/HAR20_OLS.in
harnet ./configs/RV/RecField_20/QLIKE/HARNet20_QLIKE_OLS.inStart experiments for all preset configuration files
/bin/bash run_all.shResults and TensorBoards for all experiments are saved in the ./HARNet/results folder.
The HARNet package was developed by Rafael Reisenhofer and Xandro Bayer.
The data in data/MAN_data.csv was obtained from the Oxford-Man Institute website.
If you have any questions, please contact rafael.reisenhofer@uni-bremen.de.