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Published on: January 16, 2018
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Liquidity effects on oil volatility forecasting: From fintech perspective
Shusheng Ding1, Tianxiang Cui2, Yongmin Zhang1
1School of Business, Ningbo University, Ningbo, Zhejiang, China.
Plos One
|November 29, 2021
Summary
Fintech innovations, using Genetic Programming, significantly improve financial volatility forecasting beyond traditional GARCH models. This approach also determines optimal models when new factors like liquidity are introduced.
Area of Science:
- Financial Economics
- Computational Finance
- Fintech Innovations
Background:
- Volatility forecasting is crucial in financial research.
- Traditional GARCH models are widely used but have limitations.
- Integrating new technologies like Fintech offers potential advancements.
Purpose of the Study:
- To explore Fintech's role in enhancing volatility forecasting.
- To develop a novel volatility forecasting model using Genetic Programming (GP).
- To assess the performance of the GP model against GARCH family models.
Main Methods:
- Employed Genetic Programming (GP), a metaheuristic evolutionary algorithm.
- Developed a new volatility forecasting model based on GP.
- Compared the GP model's performance against GARCH, IGARCH, and TGARCH models.
- Investigated the impact of the liquidity factor on volatility forecasting using GP.
Main Results:
- The developed GP model significantly outperformed GARCH family models.
- GP successfully determined optimal volatility forecasting model formats, even with unknown formulas.
- Demonstrated the effectiveness of GP in incorporating the liquidity factor into volatility forecasting.
Conclusions:
- Fintech, through GP, offers a powerful approach to volatility forecasting.
- GP provides a flexible method for discovering forecasting models, especially with new financial factors.
- This research highlights a breakthrough in applying GP technology within the financial domain.
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