Estimating the Value-at-Risk by Temporal VAE

• URL: http://arxiv.org/abs/2112.01896v1
• Date: Fri, 3 Dec 2021 13:20:41 GMT
• Title: Estimating the Value-at-Risk by Temporal VAE
• Authors: Robert Sicks, Stefanie Grimm, Ralf Korn, Ivo Richert
• Abstract summary: estimation of the value-at-risk (VaR) of a large portfolio of assets is an important task for financial institutions. We use a temporal VAE (TempVAE) that avoids an auto-regressive structure for the observation variables. As a result, the auto-pruning of the TempVAE works properly which also results in excellent estimation results for the VaR that beats classical GARCH-type and historical simulation approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 an auto-regressive 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 the use of a VAE prone to posterior collapse. Therefore, we propose to use annealing of the regularization to mitigate this effect. As a result, the auto-pruning of the TempVAE works properly which also results in excellent estimation results for the VaR that beats classical GARCH-type and historical simulation approaches when applied to real data.

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