Assessing Asset-Liability Risk with Neural Networks

Patrick Cheridito, John Ery and Mario V. Wüthrich
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Patrick Cheridito: RiskLab, ETH Zurich, 8092 Zurich, Switzerland
John Ery: RiskLab, ETH Zurich, 8092 Zurich, Switzerland
Mario V. Wüthrich: RiskLab, ETH Zurich, 8092 Zurich, Switzerland

Risks, 2020, vol. 8, issue 1, 1-17

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.

Keywords: asset-liability risk; risk capital; solvency calculation; value-at-risk; expected shortfall; neural networks; importance sampling (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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