Assessing Asset-Liability Risk with Neural Networks

Risks

John Ery¹  MarioV. Wüthrich¹  Patrick Cheridito¹ 

[1] RiskLab, ETH Zurich, 8092 Zurich, Switzerland;

关键词: asset-liability risk;    risk capital;    solvency calculation;    value-at-risk;    expected shortfall;    neural networks;    importance sampling;   

DOI  :  10.3390/risks8010016

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown