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arXiv · 2009.05034

Deep Replication of a Runoff Portfolio

Thomas Krabichler·Josef Teichmann
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Abstract

To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. This article presents the key notions of Deep Asset Liability Management (Deep~ALM) for a technological transformation in the management of assets and liabilities along a whole term structure. The approach has a profound impact on a wide range of applications such as optimal decision making for treasurers, optimal procurement of commodities or the optimisation of hydroelectric power plants. As a by-product, intriguing aspects of goal-based investing or Asset Liability Management (ALM) in abstract terms concerning urgent challenges of our society are expected alongside. We illustrate the potential of the approach in a stylised case.

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Thomas Krabichler, Josef Teichmann. 2020-09-10. Deep Replication of a Runoff Portfolio. https://arxiv.org/abs/2009.05034

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