Traditional correlation metrics like Pearson correlation capture only linear and symmetric relationships between variables, potentially obscuring meaningful dependencies in financial systems where nonlinearities and lead-lag effects are common. This paper introduces SVM-Based Predictive Correlation (S2C), a novel approach that redefines correlation in terms of predictive power rather than mere association. S2C accommodates nonlinearities and directional structures within data, allowing practitioners to better gauge risk and properly size allocations. We develop the mathematical foundations of S2C within the context of kernel methods and reproducing kernel Hilbert spaces (RKHSs), then present an empirical study involving financial and macroeconomic time series with different update frequencies. Our analysis demonstrates that S2C can recover both linear and nonlinear relationships, is more robust to irregular sampling than standard correlation measures, and captures directionality by revealing how one variable can predict another. We conclude with implications for portfolio construction and risk management, particularly addressing the “volatility washing” problem in portfolios containing illiquid alternative investments.