Comparing in- and out-of-sample approaches to variance decomposition-based estimates of network connectedness an application to the Italian banking system

We use methods that exploit variance decompositions from standard (rolling window) estimates of VAR(p) models to obtain estimates of network connectedness and perform an empirical comparison of the results derived under two alternative approaches: To base the decompositions on in-sample forecast errors vs. out-of-sample forecast errors derived from a separation between estimation and forecast evaluation window. Using the intraday realized variance of bank stock return, we derive novel empirical results on the systemic risk of the Italian banking system, whose network connectedness turns out to be generally high, increasing over time especially during the Great Financial and the European sovereign debt crises. However, whether a few net exporters of systemic risk may be reliably estimated turns out to depend on the methodology adopted when computing rolling window variance decompositions.