Multi-perspective option price forecasting combining parametric and non-parametric pricing models with a new dynamic ensemble framework

doi:10.1016/j.techfore.2024.123429

	Multi-perspective option price forecasting combining parametric and non-parametric pricing models with a new dynamic ensemble framework
	Guo, Jingjun1,2 ; Kang, Weiyi1 ; Wang, Yubing 1
	2024-07
在线发表日期	2024-05
发表期刊	Technological Forecasting and Social Change
卷号	204
摘要	This article introduces a dynamic ensemble framework that integrates parametric and non-parametric pricing models. Within this framework, we propose a time-varying parametric pricing model optimized using artificial intelligence algorithms. Additionally, we construct a non-parametric pricing model using a 2-dimensional convolutional neural network (2D-CNN) to capture the interactions among options, enhancing the existing non-parametric pricing model. Validation using China's SSE 50 ETF options trading data reveals several key findings: Firstly, the dynamic integration method proposed in this study not only improves prediction accuracy but also enhances stability. Secondly, previous parametric pricing models do not effectively utilize their pricing performance, while our proposed time-varying parametric pricing model significantly enhances accuracy. Lastly, the 2D-CNN model, which considers interactions among options trades, proves to be reasonable and effective, outperforming common non-parametric pricing models. The dynamic ensemble framework proposed in this study effectively combines the strengths of both parametric and non-parametric pricing models. This research serves as an important reference for risk managers, institutional investors, and other stakeholders. Furthermore, it provides valuable research ideas for future scholars in the field. © 2023
关键词	Convolutional neural networks Costs Deep learning Electronic trading Financial markets Forecasting Investments Neural network models Parameter estimation-Artificial intelligence algorithms Deep learning Dynamic ensemble Nonparametrics Option price Option price forecasting Options pricing Parameter optimization Price forecasting Pricing models
DOI	10.1016/j.techfore.2024.123429
收录类别	EI ; SSCI
ISSN	0040-1625
语种	英语
WOS研究方向	Business & Economics ; Public Administration
WOS类目	Business ; Regional & Urban Planning
WOS记录号	WOS:001237878300001
出版者	Elsevier Inc.
EI入藏号	20241916030473 ; Commerce
EI主题词	Commerce
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 723.4 Artificial Intelligence ; 723.5 Computer Applications ; 911 Cost and Value Engineering ; Industrial Economics
原始文献类型	Journal article (JA)
EISSN	1873-5509
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/36248
专题	统计与数据科学学院
通讯作者	Kang, Weiyi
作者单位	1.School of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou; 730020, China; 2.Center for Quantitative Analysis of Gansu Economic Development, Lanzhou University of Finance and Economics, Lanzhou; 730020, China
第一作者单位	统计与数据科学学院; 兰州财经大学
通讯作者单位	统计与数据科学学院
推荐引用方式 GB/T 7714	Guo, Jingjun,Kang, Weiyi,Wang, Yubing. Multi-perspective option price forecasting combining parametric and non-parametric pricing models with a new dynamic ensemble framework[J]. Technological Forecasting and Social Change,2024,204.
APA	Guo, Jingjun,Kang, Weiyi,&Wang, Yubing.(2024).Multi-perspective option price forecasting combining parametric and non-parametric pricing models with a new dynamic ensemble framework.Technological Forecasting and Social Change,204.
MLA	Guo, Jingjun,et al."Multi-perspective option price forecasting combining parametric and non-parametric pricing models with a new dynamic ensemble framework".Technological Forecasting and Social Change 204(2024).