Abstract
Classical option pricing models rely on prior assumptions made on the dynamics of the underlying assets. While empirical evidence showed that these models may partially explain the option prices, their performance may be poor when the actual situation deviates from the assumptions. Neural network models are capable of learning the underlying relationship through the data. However, to avoid over-fitting, these models require massive amount of data, which are not available for option pricing problems. We propose a new model by integrating neural networks to a classical option pricing model, thus increasing the model flexibility while requiring a reasonable amount of data. We show that the training of the model, also known as the calibration, may be formulated into a simulation optimization problem, and it may be solved in a way that is compatible to the training of neural networks. Preliminary numerical results show that our approach appears to work well.
Original language | English (US) |
---|---|
Title of host publication | 2021 Winter Simulation Conference, WSC 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665433112 |
DOIs | |
State | Published - 2021 |
Externally published | Yes |
Event | 2021 Winter Simulation Conference, WSC 2021 - Phoenix, United States Duration: Dec 12 2021 → Dec 15 2021 |
Publication series
Name | Proceedings - Winter Simulation Conference |
---|---|
Volume | 2021-December |
ISSN (Print) | 0891-7736 |
Conference
Conference | 2021 Winter Simulation Conference, WSC 2021 |
---|---|
Country/Territory | United States |
City | Phoenix |
Period | 12/12/21 → 12/15/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.