Time Series Forecasting Models for S&P 500 Financial Turbulence

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ISSN Print: 2162-2434
ISSN Online: 2162-2442
www.scirp.org/journal/jmf
E-mail: jmf@scirp.org

"Time Series Forecasting Models for S&P 500 Financial Turbulence"
written by Hugo Gobato Souto,
published by Journal of Mathematical Finance, Vol.13 No.1, 2023

has been cited by the following article(s):

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[2]	TSMixer and Realized Volatility Prediction
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[3]	Yang & Zhang's realized volatility: Automated estimation in Python
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[8]	Wasserstein Distance and Realized Volatility
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[1]	Yang & Zhang’s realized volatility: Automated estimation in Python Software Impacts, 2024 DOI:10.1016/j.simpa.2024.100613

[2]	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

[3]	Forecasting realized volatility through financial turbulence and neural networks Economics and Business Review, 2023 DOI:10.18559/ebr.2023.2.737

[4]	NHITS for Forecasting Stock Realized Volatility SSRN Electronic Journal, 2023 DOI:10.2139/ssrn.4650761

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