Fast Empirical Scenarios

• URL: http://arxiv.org/abs/2307.03927v2
• Date: Mon, 5 Feb 2024 15:04:23 GMT
• Title: Fast Empirical Scenarios
• Authors: Michael Multerer, Paul Schneider, Rohan Sen
• Abstract summary: We seek to extract a small number of representative scenarios from large and high-dimensional panel data. Among two novel algorithms, the first identifies scenarios that have not been observed before. The second proposal picks important data points from states of the world that have already realized.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We seek to extract a small number of representative scenarios from large and high-dimensional 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 picks 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 high-dimensional numerical integration. Extensive numerical benchmarking studies and an application in portfolio optimization favor the proposed algorithms.

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