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），一种用于高效且稳健地估计多元模型中时变相关性的半参数方法。我们利用非参数秩统计量，即Spearman's rho与Kendall's tau，来估计未知相关矩阵，并讨论了模型的平稳性、beta混合与rho混合条件。通过估计2013年2月1日至2025年1月23日期间纳入S&P100和S&P500指数的股票的时变条件相关矩阵，我们展示了该方法的应用。结果表明，与经典动态条件相关（DCC）模型相比，DCS改进了诊断检验，提供了不相关且服从正态分布的残差。风险管理应用显示，使用DCS模型估计的全局最小方差投资组合相比基于DCC和DCC-NL模型的投资组合具有更低的换手率，同时在S&P 100成分股构建的投资组合中实现了更高的夏普比率。