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Assessing asset-liability risk with neural networks

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  • Patrick Cheridito
  • John Ery
  • Mario V. Wuthrich

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.

Suggested Citation

  • Patrick Cheridito & John Ery & Mario V. Wuthrich, 2021. "Assessing asset-liability risk with neural networks," Papers 2105.12432, arXiv.org.
    • Handle: RePEc:arx:papers:2105.12432
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    References listed on IDEAS

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    1. Perčić, Maja & Vladimir, Nikola & Jovanović, Ivana & Koričan, Marija, 2022. "Application of fuel cells with zero-carbon fuels in short-sea shipping," Applied Energy, Elsevier, vol. 309(C).

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