An AI Model for Oil Volatility Forecasting

Wang, Fan; Lin, Junqin; Wei, Lijian

doi:10.1109/mis.2020.2972544

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…The sixth article entitled "An AI Model for Oil Volatility Forecasting" by Wang et al 8 first proposes a genetic algorithm learning with a classifier system, and then constructs an AI model for oil volatility forecasting on the basis of internal information and external information. The model provides decision support for mark-to-market portfolio and risk management by forecasting whether 1-day-ahead volatility is above a given threshold.…”

Section: Editorial Editorialmentioning

confidence: 99%

Special Issue on AI and FinTech: The Challenge Ahead

et al. 2020

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& IT IS OUR pleasure to share with you this special issue on AI and Fintech, which includes 17 articles published in the March/April (8 articles) and May/June (9 articles) issues of IEEE Intelligent Systems (IS).After our announcement in early September 2019 for this special issue on AI and Fintech for IS, we received a larger volume of manuscripts than anticipated by the January 2020 deadline. These worldwide submissions included both academic researchers and practitioners in IT and financial industries. After a long round of revisions from independent, anonymous referees that ultimately led to the current, official versions of the articles published in both issues, we would like to first and foremost thank all the contributors and the anonymous referees for their hard work on this project. Second, we give our sincere gratitude to the supportive effort of the IS team led

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Section: Editorial Editorialmentioning

confidence: 99%

Special Issue on AI and FinTech: The Challenge Ahead

et al. 2020

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Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model

Zhang,

Song,

Peng

et al. 2024

Journal of Forecasting

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Most existing studies on volatility forecasting have focused on interday characteristics and ignored intraday characteristics of high‐frequency data, especially the asymmetric impact of positive and negative jumps on volatility. In this paper, 5‐min high‐frequency data are used to construct realized volatility which is decomposed into continuous components and jump components with positive and negative directions. Then, this information is combined with the long short‐term memory model for the realized volatility prediction. The empirical analysis demonstrates that negative jumps resulting from negative news have a more significant impact on market volatility than positive jumps. Additionally, the long short‐term memory model, which incorporates positive and negative jump volatility, outperforms traditional econometric and machine learning models in predicting out‐of‐sample volatility. Furthermore, applying the prediction results to value at risk yields a better measurement effect than the generalized autoregressive conditional heteroskedasticity model.

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Innovation of an AI Classifier using different classification techniques to Predict the Placement Matrix of a University

Chandra

Kaur

2020

2020 3rd International Conference on Intelligent Sustainable Systems (ICISS)

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An AI Model for Oil Volatility Forecasting

Cited by 4 publications

References 14 publications

Special Issue on AI and FinTech: The Challenge Ahead

Special Issue on AI and FinTech: The Challenge Ahead

Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model

Innovation of an AI Classifier using different classification techniques to Predict the Placement Matrix of a University

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