Fast Empirical Scenarios

TL;DR

This paper introduces two efficient algorithms for extracting representative scenarios from large panel data, aiding interpretable decision-making under uncertainty with applications in portfolio optimization.

Contribution

The paper presents two novel algorithms for scenario extraction that handle unobserved and observed data points, improving scenario-based modeling and numerical integration.

Findings

Algorithms are computationally efficient.

Numerical benchmarking shows superior performance.

Application demonstrated in portfolio optimization.

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.