has been cited by the following article(s):
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[1]
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Enhancing financial risk management: a novel multivariate neural network approach for realized covariance matrix prediction
Financial Innovation,
2026
DOI:10.1186/s40854-025-00816-6
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[2]
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Evaluating the Efficacy of NHITS for Forecasting Stock Realized Volatility: A Comparative Analysis with Established Models
Computational Economics,
2025
DOI:10.1007/s10614-025-10917-0
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[3]
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Historical Land Cover Dynamics and Projected Changes in the High Andean Zone of the Locumba Basin: A Predictive Approach Using Remote Sensing and Artificial Neural Network—Cellular Automata Model
Land,
2025
DOI:10.3390/land14071442
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[4]
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Measuring Financial Failure in Accommodation Companies During Financial Turbulence Periods: Altman Z, Zmijewski J and Springate S Score Models
Fiscaoeconomia,
2025
DOI:10.25295/fsecon.1654377
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[5]
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Time-mixing and feature-mixing modelling for realized volatility forecast: Evidence from TSMixer model
The Journal of Finance and Data Science,
2024
DOI:10.1016/j.jfds.2024.100143
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[6]
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Yang & Zhang’s realized volatility: Automated estimation in Python
Software Impacts,
2024
DOI:10.1016/j.simpa.2024.100613
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[7]
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A novel loss function for neural network models exploring stock realized volatility using Wasserstein Distance
Decision Analytics Journal,
2024
DOI:10.1016/j.dajour.2023.100369
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[8]
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A generalization of the Topological Tail Dependence theory: From indices to individual stocks
Decision Analytics Journal,
2024
DOI:10.1016/j.dajour.2024.100512
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[9]
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Forecasting realized volatility through financial turbulence and neural networks
Economics and Business Review,
2023
DOI:10.18559/ebr.2023.2.737
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[10]
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NHITS for Forecasting Stock Realized Volatility
SSRN Electronic Journal,
2023
DOI:10.2139/ssrn.4650761
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