Pricing Analysis of Drop-Back Certificate: Comparison between Monte-Carlo Simulation and Generative Artificial Intelligence-Assisted Pricing

DOI

10.2991/978-94-6239-801-6_33How to use a DOI?

Keywords

Financial Instruments; Pricing; Monte-Carlo simulation; GenAI

Abstract

With the increasing complexity of structured financial products, traditional numerical pricing methods face significant challenges in computational efficiency and practical application. The advancement of generative artificial intelligence technology offers new approaches for financial pricing. This study takes the USD Drop-Back Certificate issued by Credit Suisse as the research subject. Methodologically, it begins by simulating the path of the S&P 500 index under the geometric Brownian motion (GBM) assumption. Subsequently, a subset of Monte-Carlo results is used to train a generative Artificial Intelligence (GenAI) model, enabling it to learn the mapping relationship between model parameters and the expected present value of the product. The model is then deployed to perform pricing predictions on out-of-sample data. Finally, the accuracy, stability, and efficiency of the AI-assisted pricing outcomes are evaluated. The findings reveal that AI-assisted pricing achieves a high degree of consistency with Monte-Carlo results while substantially reducing single-instance pricing computation time, with efficiency improvements reaching several orders of magnitude. This research indicates that GenAI can serve as an effective augmentation to Monte-Carlo simulation, offering a more efficient and equally reliable technical pathway for the rapid pricing of complex structured products.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Haocheng Fu
AU  - Zhourui Shen
AU  - Kaili Wang
AU  - Jingheng Xu
PY  - 2026
DA  - 2026/10/23
TI  - Pricing Analysis of Drop-Back Certificate: Comparison between Monte-Carlo Simulation and Generative Artificial Intelligence-Assisted Pricing
BT  - Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)
PB  - Atlantis Press
SP  - 292
EP  - 300
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-801-6_33
DO  - 10.2991/978-94-6239-801-6_33
ID  - Fu2026
ER  -