Abstract
Recent studies indicate that systemic risk has predictive power over severe economic downturns. We propose a novel methodology that employs sparsity and targeting approaches to optimally select and combine systemic risk measures to forecast the tail of a given economic variable. Out-of-sample analysis shows that the optimal combination of systemic risk metrics may vary over time, forecasting horizons and economic proxies. Moreover, a few systemic risk measures contain all the important information for capturing the relation between systemic risk and real economy; therefore, a fixed and static combination approach may not be optimal, and the flexible parsimonious extension we introduce leads to improvement in forecasting performance.