English

Conditioning on a Volatility Proxy Compresses the Apparent Timescale of Collective Market Correlation

Computational Finance 2026-03-17 v1 Statistical Mechanics Machine Learning

Abstract

We address the attribution problem for apparent slow collective dynamics: is the observed persistence intrinsic, or inherited from a persistent driver? For the leading eigenvalue fraction ψ1=λmax/N\psi_1=\lambda_{\max}/N of S\&P 500 60-day rolling correlation matrices (237237 stocks, 2004--2023), a VIX-coupled Ornstein--Uhlenbeck model reduces the effective relaxation time from 298298 to 6161 trading days and improves the fit over bare mean reversion by Δ\DeltaBIC=109=109. On the decomposition sample, an informational residual of log(VIX)\log(\mathrm{VIX}) alone retains most of that gain (Δ\DeltaBIC=78.6=78.6), whereas a mechanical VIX proxy alone does not improve the fit. Autocorrelation-matched placebo fields fail (Δ\DeltaBICmax=2.7_{\max}=2.7), disjoint weekly reconstructions still favor the field-coupled model (Δ\DeltaBIC=140=140--151151), and six anchored chronological holdouts preserve the out-of-sample advantage. Quiet-regime and field-stripped residual autocorrelation controls show the same collapse of persistence. Stronger hidden-variable extensions remain only partially supported. Within the tested stochastic class, conditioning on the observed VIX proxy absorbs most of the apparent slow dynamics.

Keywords

Cite

@article{arxiv.2603.14072,
  title  = {Conditioning on a Volatility Proxy Compresses the Apparent Timescale of Collective Market Correlation},
  author = {Yuda Bi and Vince D Calhoun},
  journal= {arXiv preprint arXiv:2603.14072},
  year   = {2026}
}