Systemic risk spillover dynamics and causality structures in composite stress networks

Abstract This study examines systemic risk within the Iranian economic ecosystem by analyzing the interactions between different sectors from January 2013 to October 2022. A new systemic risk stress indicator is developed based on five key market indicators: money, equity, financial intermediaries, foreign exchange (FX), and oil markets, which are constructed using various sub-indices. Granger causality networks are used to explore the relationships between these indicators, and the results are validated by mutual information (MI)-based networks for sub-index selection. The results are further validated by an improved exponentially weighted moving average indicator that utilizes entropy distance. Additionally, a novel systemic risk stress indicator has been developed based on Granger causality findings. This indicator incorporates MI to capture time-dependent causal relationships and to analyze the interdependencies between various economic and financial indicators. The findings reveal significant bidirectional causality between specific indicators, with strong evidence of cross-market spillovers. Notably, oil price shocks and exchange rate fluctuations have substantial effects on economic stability. The analysis highlights key interconnections within the financial markets, including a significant causal link between the money market and the FX market, primarily driven by central bank interventions. The equity market also influences both the FX and oil markets, with capital outflows and sectoral composition playing a role in these dynamics. Additionally, the financial intermediaries market impacts market sentiment, and a causal relationship between FX and oil prices is established. These results suggest that policymakers and investors should consider these interconnected relationships when formulating strategies to mitigate risks in volatile economic environments. The study also calls for further research into the microstructure of sub-indices across different markets to better understand these complex dynamics.