We propose a general framework for measuring frequency dynamics of connectedness in economic variables based on spectral representation of variance decompositions . We argue that the frequency dynamics is insightful when studying the connectedness of variables as shocks with heterogeneous frequency responses will create frequency dependent connections of different strength that remain hidden when time domain measures are used. Two applications support the usefulness of the discussion, guide a user to apply the methods in different situations, and contribute to the literature with important findings about sources of connectedness. Giving up the assumption of global stationarity of stock market data and approximating the dynamics locally , we document rich time-frequency dynamics of connectedness in US market risk in the first application. Controlling for common shocks due to common stochastic trends which dominate the connections, we identify connections of global economy at business cycle frequencies of 18 up to 96 months in the second application. In addition, we study the effects of cross-sectional dependence on the connectedness of variables .
Risk management has generally focused on aggregate connectedness, overlooking its cyclical sources . We argue that the frequency dynamics is insightful for studying this connectedness because shocks with heterogeneous frequency responses create linkages with various degrees of persistence. Such connections are important for understanding the possible sources of systemic risk specific to economic cycles but remain hidden when aggregate measures of connectedness are used. To estimate connectedness on short-, medium-, and long-term financial cycles, we propose a general framework based on spectral representation of variance decompositions. In an empirical application , we document the rich dynamics of volatility connectedness in the US financial institutions with short-term connections due to contemporaneous correlations as well as significant weekly, monthly, and longer connections that play a role. Hence, we find that the financial market clears part of the information but that the permanent changes in investors' expectations having longer-term responses are non-negligible .
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We propose a general framework for measuring frequency dynamics of connectedness in economic variables based on spectral representation of variance decompositions
Risk management has generally focused on aggregate connectedness, overlooking its cyclical sources
R
when studying the connectedness of variables as
for studying this connectedness because
R
will create frequency dependent connections of different strength that
create linkages with various degrees of persistence. Such connections are important for understanding the possible sources of systemic risk specific to economic cycles but
R
time domain measures
aggregate measures of connectedness
R
Two applications support the usefulness of the discussion, guide a user to apply the methods in different situations, and contribute to the literature with important findings about sources of connectedness. Giving up the assumption of global stationarity of stock market data and approximating the dynamics locally
To estimate connectedness on short-, medium-, and long-term financial cycles, we propose a general framework based on spectral representation of variance decompositions. In an empirical application
R
rich time-frequency dynamics of connectedness in US market risk in the first application. Controlling for common shocks due to common stochastic trends which dominate the connections, we identify connections of global economy at business cycle frequencies of 18 up to 96 months in the second application. In addition, we study the effects of cross-sectional dependence on the connectedness of variables
the rich dynamics of volatility connectedness in the US financial institutions with short-term connections due to contemporaneous correlations as well as significant weekly, monthly, and longer connections that play a role. Hence, we find that the financial market clears part of the information but that the permanent changes in investors' expectations having longer-term responses are non-negligible