Deep Learning Stock Volatility with Google Domestic Trends
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- Andrea Bucci, 2020.
"Realized Volatility Forecasting with Neural Networks,"
Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
- Andrea Bucci, 0. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
- Bucci, Andrea, 2019. "Realized Volatility Forecasting with Neural Networks," MPRA Paper 95443, University Library of Munich, Germany.
- Lucien Boulet, 2021. "Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs," Papers 2109.01044, arXiv.org.
- Shujian Liao & Jian Chen & Hao Ni, 2021. "Forex Trading Volatility Prediction using Neural Network Models," Papers 2112.01166, arXiv.org, revised Dec 2021.
- Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
- Emilia Fraszka-Sobczyk & Aleksandra Zakrzewska, 2025. "The Impact of Foreign Stock Market Indices on Predictions Volatility of the WIG20 Index Rates of Return Using Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2761-2774, May.
- Yuping Song & Xiaolong Tang & Hemin Wang & Zhiren Ma, 2023. "Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH‐MIDAS and deep learning models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 51-59, January.
- Andrea Bucci, 2020.
"Cholesky–ANN models for predicting multivariate realized volatility,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 865-876, September.
- Bucci, Andrea, 2019. "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper 95137, University Library of Munich, Germany.
- Milan Cibuľa & Michal Tkáč, 2023. "Porovnanie algoritmov strojového učenia pre tvorbu predikčného modelu ceny bitcoinu [Comparison of Machine Learning Algorithms for Creation of a Bitcoin Price Prediction Model]," Politická ekonomie, Prague University of Economics and Business, vol. 2023(5), pages 496-517.
- Chao Liu & Fengfeng Gao & Mengwan Zhang & Yuanrui Li & Cun Qian, 2024. "Reference Vector-Based Multiobjective Clustering Ensemble Approach for Time Series Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 181-210, July.
- Zhengyong Jiang & Jeyan Thiayagalingam & Jionglong Su & Jinjun Liang, 2023. "CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy," Papers 2310.01319, arXiv.org.
- Nikita Medvedev & Zhiguang Wang, 2022. "Multistep forecast of the implied volatility surface using deep learning," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(4), pages 645-667, April.
- Milan Cibuľa & Michal Tkáč, . "Porovnanie algoritmov strojového učenia pre tvorbu predikčného modelu ceny bitcoinu [Comparison of Machine Learning Algorithms for Creation of a Bitcoin Price Prediction Model]," Politická ekonomie, Prague University of Economics and Business, vol. 0.
- Yuping Song & Bolin Lei & Xiaolong Tang & Chen Li, 2024. "Volatility forecasting for stock market index based on complex network and hybrid deep learning model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 544-566, April.
- Theodoros Zafeiriou & Dimitris Kalles, 2024. "Comparative analysis of neural network architectures for short-term FOREX forecasting," Papers 2405.08045, arXiv.org.
- Manuel Nunes & Enrico Gerding & Frank McGroarty & Mahesan Niranjan, 2020. "Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box," Papers 2005.02217, arXiv.org.
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This paper has been announced in the following NEP Reports:- NEP-CMP-2015-12-20 (Computational Economics)
- NEP-MAC-2015-12-20 (Macroeconomics)
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