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Estimating the Value-at-Risk by Temporal VAE

Author

Listed:
  • Robert Buch

    (Department of Financial Mathematics, Fraunhofer ITWM, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany)

  • Stefanie Grimm

    (Department of Financial Mathematics, Fraunhofer ITWM, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany)

  • Ralf Korn

    (Department of Mathematics, RPTU Kaiserslautern-Landau, Gottlieb-Daimler-Straße 48, 67663 Kaiserslautern, Germany)

  • Ivo Richert

    (Department of Financial Mathematics, Fraunhofer ITWM, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany)

Abstract

Estimation of the value-at-risk (VaR) of a large portfolio of assets is an important task for financial institutions. As the joint log-returns of asset prices can often be projected to a latent space of a much smaller dimension, the use of a variational autoencoder (VAE) for estimating the VaR is a natural suggestion. To ensure the bottleneck structure of autoencoders when learning sequential data, we use a temporal VAE (TempVAE) that avoids the use of an autoregressive structure for the observation variables. However, the low signal-to-noise ratio of financial data in combination with the auto-pruning property of a VAE typically makes use of a VAE prone to posterior collapse. Therefore, we use annealing of the regularization to mitigate this effect. As a result, the auto-pruning of the TempVAE works properly, which also leads to excellent estimation results for the VaR that beat classical GARCH-type, multivariate versions of GARCH and historical simulation approaches when applied to real data.

Suggested Citation

  • Robert Buch & Stefanie Grimm & Ralf Korn & Ivo Richert, 2023. "Estimating the Value-at-Risk by Temporal VAE," Risks, MDPI, vol. 11(5), pages 1-26, April.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:5:p:79-:d:1130370
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    References listed on IDEAS

    as
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