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.

翻译：本文提出动态条件SKEPTIC（DCS）方法，这是一种用于高效稳健估计多元模型中时变相关性的半参数方法。我们利用基于秩的非参数统计量（即Spearman's rho和Kendall's tau）来估计未知相关矩阵，并讨论了模型的平稳性、β混合和ρ混合条件。通过估计2013年1月2日至2025年1月23日期间标普100和标普500成分股的时变条件相关矩阵，我们对该方法进行了实证说明。结果表明，与经典动态条件相关（DCC）模型相比，DCS方法改善了诊断检验效果，提供了不相关且服从正态分布的残差。风险管理应用显示：基于DCS模型估计的全局最小方差投资组合相比DCC和DCC-NL模型具有更低的换手率，同时使用标普100成分股构建的投资组合还能获得更高的夏普比率。