Systemic risk due to common risk exposure: models, chaotic dynamics, and Granger tail networks

I present some of our recent theoretical and empirical research on the role of common risk exposure on financial systemic risk. First, I present an analytically tractable model showing how basic common practices of accounting and risk management are able to destabilize the financial market, when feedback effects and illiquidity are taken into account. We show how changes in the constraints of the bank portfolio optimization endogenously drive the dynamics of the balance sheet aggregate of financial institutions and creates systemic risk. The model shows that when financial innovation reduces the cost of diversification below a given threshold, the strength (due to higher leverage) and coordination (due to similarity of bank portfolios) of feedback effects increase. Under fairly general assumptions on the institution’s expectations on future asset volatility and correlation, we observe that when the diversification cost is decreased or the VaR constraint is loosened, the dynamics of the system develops cycles and eventually display a chaotic behavior. Further decrease triggers a transition to a non stationary dynamics characterized by steep growths (bubbles) and plunges (bursts) of market prices. As an empirical application of this approach we introduce an econometric method to detect and analyze events of flight-to-quality by financial institutions. Specifically, using the recently proposed test for the detection of Granger causality in risk, we construct a bipartite network of systemically important banks and sovereign bonds, where the presence of a link between two nodes indicates the existence of a tail causal relation. We document that, during the recent Eurozone crisis, banks with a considerable systemic importance have significantly impacted the sovereign debt market chasing the top-quality government bonds. Finally, an out of sample analysis shows that connectedness and centrality network metrics have a significant cross-sectional forecasting power of bond quality measures.