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.

翻译：我们旨在从大规模高维面板数据中提取少量与样本矩一致的代表性情景。在两种新颖算法中，第一种算法能够识别此前未观测到的情景，并提供基于情景的协方差矩阵表示。第二种方案则从已实现的世界状态中选取重要数据点，这些数据点与高阶样本矩信息保持一致。两种算法均计算高效，适用于一致的情景建模和高维数值积分。广泛的数值基准测试研究以及投资组合优化的应用案例均支持所提出的算法。