Diagnosing and Stabilizing Dynamic Correlations in Multivariate Stochastic Volatility Models

Hongfei Guo, Juan Miguel Marín Díazaraque and Helena Veiga

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: Adding flexibility to a multivariate volatility model can worsen covariance forecasts; we show when, and how to detect it. We decompose the multivariate QLIKE loss into trace, marginal-scale, and correlation log-determinant components; counterfactual block substitutions attribute gains or losses to either part. In Bayesian dynamic-correlation stochastic volatility, the diagnostic yields a sequence: diffuse dynamic correlations can underperform a constant-correlation baseline; a stabilizing prior repairs the correlation component; once stabilized, lagged realized-volatility inputs are the strongest remaining lever, with neural corrections competitive but not dominant. Under a rolling protocol, the stabilized realized-augmented family rivals realized-covariance benchmarks while retaining full predictive densities.

Keywords: Covariance; forecasting; Dynamic; correlations; Forecast-object; diagnostics; Neural; networks; Prior; regularization; Realized; volatility; Stochastic; volatility (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 C58 G17 (search for similar items in EconPapers)
Date: 2026-07-28
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