Addressing the economic and demographic complexity via a neural network approach: risk measures for reverse mortgages

E. Lorenzo (), G. Piscopo () and M. Sibillo ()
Additional contact information
E. Lorenzo: University of Naples Federico II
G. Piscopo: University of Naples Federico II
M. Sibillo: University of Salerno

Computational Management Science, 2024, vol. 21, issue 1, No 11, 22 pages

Abstract: Abstract The study deals with the application of a neural network algorithm for fronting and solving problems connected with the riskiness in financial contexts. We consider a specific contract whose characteristics make it a paradigm of a complex financial transaction, that is the Reverse Mortgage. Reverse Mortgages allow elderly homeowners to get a credit line that will be repaid through the selling of their homes after their deaths, letting them continue to live there. In accordance with regulatory guidelines that direct prudent assessments of future losses to ensure solvency, within the perspective of the risk assessment of Reverse Mortgage portfolios, the paper deals with the estimation of the Conditional Value at Risk. Since the riskiness is affected by nonlinear relationships between risk factors, the Conditional Value at Risk is estimated using Neural Networks, as they are a suitable method for fitting nonlinear functions. The Conditional Value at Risk estimated by means of Neural Network approach is compared with the traditional Value at Risk in a numerical application.

Keywords: Neural network quantile regression; VaR; CoVaR; Reverse mortgage; Longevity risk; House price risk (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10287-023-00491-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:comgts:v:21:y:2024:i:1:d:10.1007_s10287-023-00491-x

Ordering information: This journal article can be ordered from
http://www.springer. ... ch/journal/10287/PS2

DOI: 10.1007/s10287-023-00491-x

Access Statistics for this article

Computational Management Science is currently edited by Ruediger Schultz

More articles in Computational Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().