Assessing asset-liability risk with neural networks

Artikel

We introduce a neural network approach for assessing the risk of a portfolio of assets and liabilities over a given time period. This requires a conditional valuation of the portfolio given the state of the world at a later time, a problem that is particularly challenging if the portfolio contains structured products or complex insurance contracts which do not admit closed form valuation formulas. We illustrate the method on different examples from banking and insurance. We focus on value-at-risk and expected shortfall, but the approach also works for other risk measures.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Risks ; ISSN: 2227-9091 ; Volume: 8 ; Year: 2020 ; Issue: 1 ; Pages: 1-17 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft
• Subject: importance sampling ; asset-liability risk ; expected shortfall ; neural networks ; risk capital ; solvency calculation ; value-at-risk

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Cheridito, Patrick ; Ery, John ; Wüthrich, Mario V.
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2020

Further information

• DOI: doi:10.3390/risks8010016
• Handle: http://hdl.handle.net/10419/257971
• Last update: 10.03.2025, 11:41 AM CET