Pricing European Options with Deep Learning Models

被引：3

作者：

Paredes, Meritxell Sedo ^{[1
]}

Kadry, Seifedine ^{[1
]}

机构：

[1] Noroff Univ Coll, Dept Appl Data Sci, Kristiansand, Norway

来源：

2022 FIFTH INTERNATIONAL CONFERENCE OF WOMEN IN DATA SCIENCE AT PRINCE SULTAN UNIVERSITY (WIDS-PSU 2022) | 2022年

关键词：

Artificial neural networks; deep learning; option pricing; Black-Scholes; MLP; LSTM; CNN; NEURAL-NETWORKS;

D O I：

10.1109/WiDS-PSU54548.2022.00033

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Applications of artificial neural networks for valuing financial options have a long history in academic research. When appropriately trained, these artificial neural networks have proved to obtain better results than traditional parametric models like Black-Scholes. This short paper intends to review the input variables and specific architecture of a multi-layer perceptron, long-short term memory and a convolutional neural network model for the purpose of pricing options. Additionally, it describes the data gathering process for the dataset that will be used to train these models.

引用

页码：106 / 111

页数：6