Time-sequencing European options and pricing with deep learning – Analyzing based on interpretable ALE method
作者：
Highlights：
• European option datasets were time-sequenced by maturity to extract time information.
• Two deep learning models were built for the option pricing tasks.
• Our models can price option accurately without volatility calibration and simulation.
• An interpretable method was used to find how models improved the pricing results.
• Accumulate local effect range method was proposed to get feature emphasis of models.
摘要
•European option datasets were time-sequenced by maturity to extract time information.•Two deep learning models were built for the option pricing tasks.•Our models can price option accurately without volatility calibration and simulation.•An interpretable method was used to find how models improved the pricing results.•Accumulate local effect range method was proposed to get feature emphasis of models.
论文关键词：European option,Option pricing,Deep learning,LSTM neural network,1D-CNN neural network,Interpretable machine learning
论文评审过程：Received 17 December 2020, Revised 19 September 2021, Accepted 19 September 2021, Available online 25 September 2021, Version of Record 5 October 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115951
