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Dynamic Conditional SKEPTIC
[Submitted on 12 Dec 2025 (v1), last revised 24 Jun 2026 (this v · 2026-06-25 · via stat updates on arXiv.org

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Abstract:We introduce the Dynamic Conditional SKEPTIC (DCS), a semiparametric approach for efficiently and robustly estimating time-varying correlations in multivariate models. We exploit nonparametric rank-based statistics, namely Spearman's rho and Kendall's tau, to estimate the unknown correlation matrix and discuss the stationarity, beta- and rho- mixing conditions of the model. We illustrate the methodology by estimating the time-varying conditional correlation matrix of the stocks included in the S&P100 and S&P500 during the period from 02/01/2013 to 23/01/2025. The results show that DCS improves diagnostic checks compared to the classical Dynamic Conditional Correlation (DCC) models, providing uncorrelated and normally distributed residuals. A risk management application shows that global minimum variance portfolios estimated using the DCS model exhibit lower turnover than those based on the DCC and DCC-NL models, while also achieving higher Sharpe ratios for portfolios constructed from S&P 100 constituents.

Submission history

From: Gabriele Di Luzio [view email]
[v1] Fri, 12 Dec 2025 15:26:34 UTC (935 KB)
[v2] Fri, 6 Feb 2026 15:25:59 UTC (934 KB)
[v3] Wed, 24 Jun 2026 12:27:42 UTC (942 KB)