Overcoming the Feature Selection Issue in the Pricing of American Options
24 Pages Posted: 28 Jan 2022
Date Written: January 21, 2022
The feedforward neural network Monte Carlo method (FNNMC) exhibits more robustness and
accuracy than the state-of-the-art least squares Monte Carlo method (LSM) in pricing several
American-style options. Specifically, the FNNMC price estimates are accurate for basket options,
where the FNNMC price errors are more than four times smaller than the LSM with the best
choice of basis functions. By training the neural network the FNNMC avoids the issue of choosing
a proper set of basis functions. Hence we circumvent manually engineering the features for each
type of option. Furthermore, we explore in-depth the hyperparameter selection for the FNNMC.
In the exploration, we use a novel approach called price grid search, where the search is done at
the price level instead of at the usual regression level.
Keywords: American options, option theory, least squares Monte Carlo method, deep learning, feedforward neural network Monte Carlo method
JEL Classification: G13, C02, C45, C61
Suggested Citation: Suggested Citation