Deep Neural Network Algorithms for Parabolic PIDEs and Applications in Insurance Mathematics

Rüdiger Frey () and Verena Köck ()
Additional contact information
Rüdiger Frey: Vienna University of Economics and Business, Institute for Statistics and Mathematics
Verena Köck: Vienna University of Economics and Business, Institute for Statistics and Mathematics

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2022, pp 272-277 from Springer

Abstract: Abstract In recent years a large literature on deep learning based methods for the numerical solution partial differential equations has emerged; results for integro-differential equations on the other hand are scarce. In this short paper we study deep neural network algorithms for solving linear parabolic partial integro-differential equations with boundary conditions in high dimension. To show the viability of our approach we discuss a test case study from insurance.

Keywords: Deep neural networks; Parabolic partial integro-differential equations; Machine learning; Insurance (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-99638-3_44

Ordering information: This item can be ordered from
http://www.springer.com/9783030996383

DOI: 10.1007/978-3-030-99638-3_44

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().