The paper proposes a new framework to measure financial connectedness among variables, focusing on the frequency dynamics of shocks. The authors introduce a framework based on the spectral representation of variance decompositions to estimate connectedness in short-, medium-, and long-term financial cycles. They document the rich time-frequency dynamics of volatility connectedness in US financial institutions, showing that periods of high-frequency connectedness indicate rapid and calm information processing in stock markets, while lower-frequency connectedness suggests persistent shocks transmitted over longer periods. The study contributes to the literature on systemic risk by providing a more detailed understanding of the frequency-specific sources of systemic risk. The authors use a vector autoregression model to compute variance decompositions and define frequency-dependent connectedness measures, which are then applied to US financial data. The results highlight the importance of considering different frequency bands to understand the dynamic nature of connectedness and systemic risk.The paper proposes a new framework to measure financial connectedness among variables, focusing on the frequency dynamics of shocks. The authors introduce a framework based on the spectral representation of variance decompositions to estimate connectedness in short-, medium-, and long-term financial cycles. They document the rich time-frequency dynamics of volatility connectedness in US financial institutions, showing that periods of high-frequency connectedness indicate rapid and calm information processing in stock markets, while lower-frequency connectedness suggests persistent shocks transmitted over longer periods. The study contributes to the literature on systemic risk by providing a more detailed understanding of the frequency-specific sources of systemic risk. The authors use a vector autoregression model to compute variance decompositions and define frequency-dependent connectedness measures, which are then applied to US financial data. The results highlight the importance of considering different frequency bands to understand the dynamic nature of connectedness and systemic risk.