Assessing asset-liability risk with neural networks
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Author / Producer
Date
2020-03
Publication Type
Journal Article
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yes
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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
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Publication status
published
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Book title
Journal / series
Volume
8 (1)
Pages / Article No.
16
Publisher
MDPI
Event
Edition / version
Methods
Software
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Date collected
Date created
Subject
asset-liability risk; risk capital; solvency calculation; value-at-risk; expected shortfall; neural networks; importance sampling
Organisational unit
09557 - Cheridito, Patrick / Cheridito, Patrick
08813 - Wüthrich, Mario Valentin (Tit.-Prof.)
02204 - RiskLab / RiskLab