Non-parametric insights in option pricing: a systematic review of theory, implementation and future directions

Abstract

The task of pricing options is seen as significant and receives considerable attention due to its potential to generate attractive profits through informed decision-making. Over the past few decades, researchers have extensively investigated both classical and machine-learning techniques for this purpose. Our motivation for undertaking this survey is to provide a comprehensive review and analyze systematically the recent works focusing on non-parametric models for option pricing. The analysis of the articles involves the utilization of several components such as input, output, dataset, assessment metrics, and other relevant factors. Research gaps and challenges are meticulously identified and outlined to serve as guiding insights for future improvements and advancements in the field. We categorize the implementation to assist interested researchers in easily reproducing previous studies as baselines. Based on the findings of this study, it can be inferred that the process of pricing options is a highly complicated task, requiring the consideration of several elements to enhance the accuracy and efficiency of models.