Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257971 
Year of Publication: 
2020
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 8 [Issue:] 1 [Article No.:] 16 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-17
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
importance sampling
asset-liability risk
expected shortfall
neural networks
risk capital
solvency calculation
value-at-risk
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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