This paper proposes a new framework for measuring financial connectedness and systemic risk by considering heterogeneous frequency responses to shocks. The framework is based on the spectral representation of variance decompositions, allowing for the measurement of connectedness at different frequency bands. The authors argue that understanding the frequency dynamics of connectedness is crucial for identifying the sources of systemic risk, as shocks can have varying impacts depending on their frequency. In an empirical application, the authors analyze the time-frequency dynamics of volatility connectedness in US financial institutions. They find that periods of high-frequency connectedness correspond to times when financial markets process information rapidly and shocks have short-term effects, while lower-frequency connectedness suggests persistent shocks that transmit over longer periods. The study highlights the importance of considering frequency-specific sources of connectedness when assessing systemic risk. The paper introduces a general framework for decomposing connectedness into different frequency bands, using spectral representations of variance decompositions. This approach allows for the measurement of connectedness in the frequency domain, providing insights into the long-term, medium-term, and short-term impacts of shocks. The authors also discuss the importance of cross-sectional correlations in influencing connectedness and the need for frequency-specific measures to capture the true dynamics of systemic risk. The study concludes that the frequency-specific sources of connectedness are essential for understanding systemic risk in financial systems. By analyzing the time-frequency dynamics of connectedness, the authors demonstrate that different frequency bands play varying roles at different times, and that understanding these dynamics is crucial for policymakers and regulators in monitoring and managing systemic risk. The paper provides a comprehensive framework for measuring connectedness in the frequency domain, which can be applied to various financial systems and markets.This paper proposes a new framework for measuring financial connectedness and systemic risk by considering heterogeneous frequency responses to shocks. The framework is based on the spectral representation of variance decompositions, allowing for the measurement of connectedness at different frequency bands. The authors argue that understanding the frequency dynamics of connectedness is crucial for identifying the sources of systemic risk, as shocks can have varying impacts depending on their frequency. In an empirical application, the authors analyze the time-frequency dynamics of volatility connectedness in US financial institutions. They find that periods of high-frequency connectedness correspond to times when financial markets process information rapidly and shocks have short-term effects, while lower-frequency connectedness suggests persistent shocks that transmit over longer periods. The study highlights the importance of considering frequency-specific sources of connectedness when assessing systemic risk. The paper introduces a general framework for decomposing connectedness into different frequency bands, using spectral representations of variance decompositions. This approach allows for the measurement of connectedness in the frequency domain, providing insights into the long-term, medium-term, and short-term impacts of shocks. The authors also discuss the importance of cross-sectional correlations in influencing connectedness and the need for frequency-specific measures to capture the true dynamics of systemic risk. The study concludes that the frequency-specific sources of connectedness are essential for understanding systemic risk in financial systems. By analyzing the time-frequency dynamics of connectedness, the authors demonstrate that different frequency bands play varying roles at different times, and that understanding these dynamics is crucial for policymakers and regulators in monitoring and managing systemic risk. The paper provides a comprehensive framework for measuring connectedness in the frequency domain, which can be applied to various financial systems and markets.