@article{Høverstad2025, 
author = {Boye A. Høverstad and Morten Risstad and Lavrans K. Sagen},
title = {Transformers vs. LSTM-MLP for option pricing},
year = {2025},
journal = {AIMS Mathematics},
volume = {10},
number = {11},
pages = {27152-27170},
keywords = {option pricing, deep learning, LSTM-MLP, transformers},
url = {https://www.sciopen.com/article/10.3934/math.20251193},
doi = {10.3934/math.20251193},
abstract = {In the realm of option pricing, parametric models originating from the Black-Scholes-Merton framework have proven extremely persistent. However, machine learning models have recently entered the field with success, arguably due to their flexible and non-parametric nature. A combined LSTM-MLP deep learning architecture that combines time series data with cross-sectional pricing information, avoiding explicit volatility estimates, has recently been proposed. This LSTM-MLP model outperforms relevant benchmarks in different dimensions. In this research, we investigated whether a transformer-based alternative is able to better capture the inter-temporal characteristics of the data than the LSTM-based LSTM-MLP model. We found that although the transformer performs better during the extreme market conditions of COVID-19, the LSTM-MLP architecture is overall superior.}
}