Regime-Aware Estimation of Large Equity Correlation Matrices: Tail-Pairwise Dependence and Dynamic Conditional Correlation Decomposition for Water and Energy Sectors
Abstract
Equity correlation matrices are foundational to quantitative risk, yet the operational default of a single Gaussian-copula structure fit to the full sample has a structural defect: it forces asymptotic tail-independence and so cannot represent the joint moves that drive risk in stress episodes. We argue that the dependence of equity returns is not one object but three distinct under lower-tail, central, and upper-tail conditions, and that any estimator aimed at tail-sensitive applications should be regime-aware. We develop a decomposition that fits a dedicated dependence matrix to each regime, using a tail-dependence estimator grounded in multivariate regular variation in the two tails and a time-varying conditional-correlation model in the central regime, and recombines the three at the unconditional regime probabilities to form a steady-state matrix. The construction identifies tail-regime dependence that the Gaussian-copula benchmark structurally suppresses. Demonstrated on water- and energy-sector Exchange Traded Funds (ETF) constituents, the tail regimes reorganize the commodity partition produced by the central regime, identifying the pairs where the Gaussian-copula default systematically fails to identify joint extreme co-movement.
Related
- Signal Correlation, IC, and PnL Dependence
- BRACE: Blockwise Rank Aggregation for Correlation Estimation
- Robust Estimation under Spurious Correlation via Statistical Counterfactual Augmentation
- Conditional Decomposition Discrepancy: Separating Conditional Bias from Sampling Bias in Small-Sample Cross-Domain Cost Forecasting
- The correlation energy of the electron gas in the mean-field regime
BibTeX
@misc{ensor2026regimeaware,
title = {{Regime-Aware Estimation of Large Equity Correlation Matrices: Tail-Pairwise Dependence and Dynamic Conditional Correlation Decomposition for Water and Energy Sectors}},
author = {Katherine B. Ensor and Quadri Popoola and Meredith Kruse and Daniel S. Cooley},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.12166},
eprint = {2610.12166},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.12166},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Regime-Aware Estimation of Large Equity Correlation Matrices: Tail-Pairwise Dependence and Dynamic Conditional Correlation Decomposition for Water and Energy Sectors
AU - Katherine B. Ensor
AU - Quadri Popoola
AU - Meredith Kruse
AU - Daniel S. Cooley
PY - 2026
DA - 2026/10/08
PB - arXiv
DO - 10.48550/arxiv.2610.12166
UR - https://arxiv.org/abs/2610.12166
AB - Equity correlation matrices are foundational to quantitative risk, yet the operational default of a single Gaussian-copula structure fit to the full sample has a structural defect: it forces asymptotic tail-independence and so cannot represent the joint moves that drive risk in stress episodes. We argue that the dependence of equity returns is not one object but three distinct under lower-tail, central, and upper-tail conditions, and that any estimator aimed at tail-sensitive applications should be regime-aware. We develop a decomposition that fits a dedicated dependence matrix to each regime, using a tail-dependence estimator grounded in multivariate regular variation in the two tails and a time-varying conditional-correlation model in the central regime, and recombines the three at the unconditional regime probabilities to form a steady-state matrix. The construction identifies tail-regime dependence that the Gaussian-copula benchmark structurally suppresses. Demonstrated on water- and energy-sector Exchange Traded Funds (ETF) constituents, the tail regimes reorganize the commodity partition produced by the central regime, identifying the pairs where the Gaussian-copula default systematically fails to identify joint extreme co-movement.
ER -