SHS Web Conf.
Volume 181, 20242023 International Conference on Digital Economy and Business Administration (ICDEBA 2023)
|Number of page(s)
|Digital Transformation and Emerging Technologies
|17 January 2024
Risk prediction of interest rate futures based on machine learning scenarios
1 Information Engineering School, Shanghai Maritime University, 200135 Shanghai, China
2 France Sino-European School of Technology, Shanghai University, 200444 Shanghai, China
3 College of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, 311300 Hangzhou, China
4 Electrical and Computer Engineering, Shenzhen MSU-BIT University, 518100 Shenzhen, China
* Corresponding author: firstname.lastname@example.org
The interest rate futures market is a significant part of the financial market. It has a crucial impact on forecast the interest rate risk in global financial markets, which due to the complexity of financial markets and the volatility of interest rate futures. Based on machine learning scenarios to analyse and compare different algorithms, this paper analyses and forecast 2-year Treasury futures for the period 2022.6-2023.6 through regressions and other methods. Meanwhile, it is applied to construct charts and graphs to better compare and analyse models that are more suitable for forecasting future risk in interest rate futures. National policies, the volatility of the general market environment and its smoothness are utilized as the main factors to forecast its risk fluctuations. The main algorithms this paper use are: random forest regression, ARIMA model, BP Neural Network regression model, ARCH model (model validity test), GARCH model. In conclusion, though the predicted results of the random forest and ARIMA models exhibit a close to 0 and have strong stability, the predicted results of the GARCH are relatively better, none of them achieve the desired prediction performance.
© The Authors, published by EDP Sciences, 2024
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