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We propose a tool to predict risks to economic growth and international business cycles spillovers: the gross domestic product (GDP)-Network conditional value at risk (CoVaR).Our methodology to assess Growth-at-Risk is composed of two building blocks. In the first step, we apply a machine learning methodology, namely the network-based NETS by Barigozzi and Brownlees, to identify significant linkages between pair of countries. In the second step, 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, we prove that our two-step approach outperforms alternative one-step quantile regression models in predicting risks to economic growth. Finally, we show that Global exposure to economic growth tail events is decreasing over time.

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