Journals Proceedings

Abstract

Nonparametric models such as machine learning methods that are counterparts of parametric financial models were applied to pricing American index options. 10 year S&P 100 Index American options were adopted as experimental dataset and the both training (in-sample) and test (out-of-sample) errors of machine learning and ad-hoc pricing which is a conventional financial pricing model were calculated and compared to each other. We found out that Bayesian neural network outperforms the other pricing methods. Furthermore, we suggested an ensemble method which takes advantage of both machine learning method and ad-hoc pricing method and as a consequence, it shows the best performance.