In this article, we investigate the role played by idiosyncratic networks in systemic risk transmission among global systemically important banks. To construct idiosyncratic networks, we employ the conditional Granger causality approach and find they are unstable in the short term and stable in the long term. Additionally, we visualise dynamic idiosyncratic networks and confirm that their evolutionary pattern is similar to that of systemic risk. Moreover, we further explore systemic risk and idiosyncratic networks at an individual level and determine that in‐degree and in‐strength measures negatively influence systemic risk. This indicates that a well‐connected idiosyncratic network in the banking system is prone to risk spillover and cause idiosyncratic contagion. The results of the network analysis indicate that global banking idiosyncratic networks are a complex system and can serve as a valuable reference for investors and policymakers.

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We construct a sovereign default network by employing high-dimensional vector autoregressions obtained by analyzing connectedness in sovereign credit default swap markets. We develop four measures of centrality, namely, degree, betweenness, closeness, and eigenvector centralities, to detect whether network properties drive the currency risk premia. We observe that closeness and betweenness centralities can negatively drive currency excess returns but do not exhibit a relationship with forward spread. Thus, our developed network centralities are independent of an unconditional carry trade risk factor. Based on our findings, we develop a trading strategy by taking a long position on peripheral countries’ currencies and a short position on core countries’ currencies. The aforementioned strategy generates a higher Sharpe ratio than the currency momentum strategy. Our proposed strategy is robust to foreign exchange regimes and the coronavirus disease 2019 pandemic.

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