GDP-Network CoVaR: A Tool for Assessing Growth-at-Risk

Abstract

We propose a tool to predict risks to economic growth and international business cycles spillovers: the GDP-Network CoVaR. Our methodology to assess Growth-at-Risk is composed by two building blocks. First, we apply the network-based NETS methodology by Barigozzi and Brownlees to identify significant linkages between neighbour countries. Second, applying the CoVaR methodology by Adrian and Brunnermeier, and exploiting international statistics on trade flows and GDPs, we derive the entire distribution of Economic Growth Spillover exposures at the bilateral, country and global level for different quantiles of tail events on economic growth. We find that Economic Growth Spillover probability distribution is time-varying, left-skewed and in some cases bi- or even multi-modal. Second, as in the previous contributions, we find that spillover risks are more severe during financial turmoil. Third, Global exposure to economic growth tail events is decreasing over time. Finally, we prove that our two-step approach outperforms alternative one-step quantile regression models in predicting risks to economic growth.