Fast Empirical Scenarios
This work addresses the need for interpretable decision-making under uncertainty in fields like finance, though it appears incremental as it builds on existing scenario-based methods.
The paper tackles the problem of extracting a small set of representative scenarios from large panel data that match sample moments, introducing two novel algorithms: one for identifying new scenarios with covariance representation and another for selecting important realized data points consistent with higher-order moments. The result includes efficient computation, consistent modeling, and favorable performance in numerical benchmarks and a portfolio optimization application.
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.