Optimal Stopping with Adapted Neural Networks

8 Pages Posted: 12 Dec 2019 Last revised: 13 Dec 2019

Date Written: November 22, 2019

Abstract

We use temporally adapted neural networks to solve a generalization of the optimal exercise problem for a discrete set of possible exercise times. Versions based on convolutional and attention layers were implemented, tested and found to produce state of the art results on the fractional Brownian motion with various Hurst parameters. The approach is intuitive and fully agnostic with respect to the dependency structure of the underlying stochastic process.

Keywords: optimal stopping, optimal exercise, american options, deep neural networks, convolutional networks, attention networks

JEL Classification: C41, C45, G13

Suggested Citation

Dimitroff, Georgi and Hristov, Radoslav, Optimal Stopping with Adapted Neural Networks (November 22, 2019). Available at SSRN: https://ssrn.com/abstract=3491624 or http://dx.doi.org/10.2139/ssrn.3491624

Radoslav Hristov

Goethe University Frankfurt ( email )

Grüneburgplatz 1
Frankfurt am Main, 60323
Germany

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
43
Abstract Views
265
PlumX Metrics