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Fast Empirical Scenarios

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  • Michael Multerer
  • Paul Schneider
  • Rohan Sen

Abstract

We seek to extract a small number of representative scenarios from large panel data that are consistent with sample moments. Among two novel algorithms, the first identifies scenarios that have not been observed before, and comes with a scenario-based representation of covariance matrices. The second proposal selects important data points from states of the world that have already realized, and are consistent with higher-order sample moment information. Both algorithms are efficient to compute and lend themselves to consistent scenario-based modeling and multi-dimensional numerical integration that can be used for interpretable decision-making under uncertainty. Extensive numerical benchmarking studies and an application in portfolio optimization favor the proposed algorithms.

Suggested Citation

  • Michael Multerer & Paul Schneider & Rohan Sen, 2023. "Fast Empirical Scenarios," Papers 2307.03927, arXiv.org, revised Nov 2024.
    • Handle: RePEc:arx:papers:2307.03927
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    References listed on IDEAS

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    1. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
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