Conditional Method Confidence Set
Abstract
This paper proposes a Conditional Method Confidence Set (CMCS) which allows to select the best subset of forecasting methods with equal predictive ability conditional on a specific economic regime. The test resembles the Model Confidence Set by Hansen et al. (2011) and is adapted for conditional forecast evaluation. We show the asymptotic validity of the proposed test and illustrate its properties in a simulation study. The proposed testing procedure is particularly suitable for stress-testing of financial risk models required by the regulators. We showcase the empirical relevance of the CMCS using the stress-testing scenario of Expected Shortfall. The empirical evidence suggests that the proposed CMCS procedure can be used as a robust tool for forecast evaluation of market risk models for different economic regimes.
Cite
@article{arxiv.2505.21278,
title = {Conditional Method Confidence Set},
author = {Lukas Bauer and Ekaterina Kazak},
journal= {arXiv preprint arXiv:2505.21278},
year = {2025}
}