Assessing Asset-Liability Risk With Neural Networks
Risks 2020, 8, 16.
Posted: 13 Jan 2020 Last revised: 10 Feb 2020
Date Written: December 23, 2019
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-asset liability risk, risk capital, solvency calculation, value-at-risk, expected shortfall, neural networks, importance sampling
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