Option pricing under sub-mixed fractional Brownian motion based on time-varying implied volatility using intelligent algorithms

doi:10.1007/s00500-023-08647-2

	Option pricing under sub-mixed fractional Brownian motion based on time-varying implied volatility using intelligent algorithms
	Guo, Jingjun; Kang, Weiyi; Wang, Yubing
	2023
发表期刊	Soft Computing
卷号	27 期号:20 页码:15225-15246
摘要	Against the background of the current complex international geopolitical situation and more intense trade frictions, the volatility of financial assets has important research significance as a basis for risk analysis and option pricing. First, considering the characteristics of financial assets—such as "long dependence"—the pricing model can become complicated, making it difficult to calculate the implied volatility directly. Establishing the loss function between the trading data and modeled value, the implied volatility at different moments solved using the global optimal double annealing algorithm was found to differ from the generalized autoregressive conditional heteroskedasticity (GARCH) volatility and historical volatility. Second, the implied volatility considering people’s future expectations of financial assets was predicted using the previously known implied volatility via deep learning methods. The empirical results showed that the implied volatilities predicted using the long short-term memory (LSTM) and one-dimensional convolutional neural network (1D-CNN) methods performed well for option pricing. Moreover, the fractal option-pricing models outperformed the traditional Black–Scholes (B–S) pricing model. Finally, based on the accumulated local effect (ALE) algorithm—which can quantify the impact analysis of different volatilities on pricing models—it was found that the predicted implied volatility using artificial intelligence algorithms was more relevant to the truth. A combination of traditional mathematical models and emerging intelligent algorithms are promoted in this study, providing a reference for investors and risk managers and contributing to the continued development of financial markets. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
关键词	Brownian movement Commerce Convolutional neural networks Costs Financial markets Investments Learning systems Risk analysis Risk assessment Artificial intelligence algorithms Deep learning Financial assets Implied volatility Intelligent Algorithms Mixed fractional Brownian motion Options pricing Pricing models Sub-mixed fractional brownian motion Time varying
DOI	10.1007/s00500-023-08647-2
收录类别	EI ; SCIE
ISSN	1432-7643
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:001016178700003
出版者	Springer Science and Business Media Deutschland GmbH
EI入藏号	20232614303672
EI主题词	Long short-term memory
EI分类号	801.3 Colloid Chemistry ; 911 Cost and Value Engineering ; Industrial Economics ; 914.1 Accidents and Accident Prevention ; 922 Statistical Methods
原始文献类型	Article in Press
EISSN	1433-7479
引用统计	被引频次：4[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/34538
专题	统计与数据科学学院
作者单位	School of Statistics, Lanzhou University of Finance and Economics, Lanzhou; 730020, China
第一作者单位	统计与数据科学学院
推荐引用方式 GB/T 7714	Guo, Jingjun,Kang, Weiyi,Wang, Yubing. Option pricing under sub-mixed fractional Brownian motion based on time-varying implied volatility using intelligent algorithms[J]. Soft Computing,2023,27(20):15225-15246.
APA	Guo, Jingjun,Kang, Weiyi,&Wang, Yubing.(2023).Option pricing under sub-mixed fractional Brownian motion based on time-varying implied volatility using intelligent algorithms.Soft Computing,27(20),15225-15246.
MLA	Guo, Jingjun,et al."Option pricing under sub-mixed fractional Brownian motion based on time-varying implied volatility using intelligent algorithms".Soft Computing 27.20(2023):15225-15246.