It takes more than two to tango: Multiple bank lending,asset commonality and risk

Multiple-bank lending is the most prevalent form of bank-firm credit relationships in nearly all countries. It results in high asset commonality and interconnectedness, allows idiosyncratic risks to become systemic, and makes the banking system more fragile and vulnerable to shocks. Using detailed, granular-level, supervisory data on large corporate loans, we show that multiple bank lending is driven, inter alia, by regulatory limits on large credit exposures. These limits, aimed at mitigating an individual bank's concentration risk, force firms to explore alternative sources of funding, making the common borrowers' phenomenon more prominent. We find that multiple bank lending is determined endogenously, and its likelihood increases with the level of portfolio similarity between lenders. The size of the original lender and its systemic importance magnifies this effect. We argue that banks do not internalize the systemic effect of their lending decisions and that multiple bank lending constitutes an insurance mechanism related to an implicit "too-many-to-fail" guarantee. Its externalities are suboptimal and should be reinforced with better monitoring by the related authorities.     

Risk spillovers and interconnectedness between systemically important institutions

In this paper, we gauge the degree of interconnectedness and quantify the linkages between global and other systemically important institutions, and the global financial system. We document that the two groups and the financial system become more interconnected during the global financial crisis when linkages across groups grow. In contrast, during tranquil times linkages within groups prevail. Global systemically important banks (G-SIBs) contribute most to system-wide distress but are also most exposed. There are more links coming from G-SIBs to other systemically important institutions (O-SIIs) than the other way around, confirming the role of G-SIBs as major risk transmitters in the financial system. The two groups and the global financial system tend to co-vary for periods up to 60 days Prior to their official designation as G-SIBs or O-SIIs, the prevalent news sentiment about these institutions (we measure with a textual analysis) was negative. Importantly, the systemic importance and exposure of G-SIBs and O-SIIs is perceived differently by the Financial Stability Board (FSB) and the European Banking Authority (EBA).     

False dichotomy alert: Improving subjective-probability estimates vs. raising awareness of systemic risk

Taleb et al. (2022) portray the superforecasting research program as a masquerade that purports to build “survival functions for tail assessments via sports-like tournaments.” But that never was the goal. The program was designed to help intelligence analysts make better probability judgments, which required posing rapidly resolvable questions. From a signal detection theory perspective, the superforecasting and Taleb et al. programs are complementary, not contradictory (a point Taleb and Tetlock (2013) recognized). The superforecasting program aims at achieving high hit rates at low cost in false-positives, whereas Taleb et al. prioritize alerting us to systemic risk, even if that entails a high false-positive rate. Proponents of each program should, however, acknowledge weaknesses in their cases. It is unclear: (a) how Taleb et al. (2022) can justify extreme error-avoidance trade-offs, without tacit probability judgments of rare, high-impact events; (b) how much superforecasting interventions can improve probability judgments of such events.     

Systemic risk of Chinese financial institutions and asset price bubbles

Although there has not been a large-scale systemic crisis in China, high-risk financial events have occurred continuously in recent years. This research thus creatively analyzes the determinants of systemic risk for Chinese financial institutions from the view of asset price bubbles. First, we identify bubbles in the China stock and real estate markets on the basis of the generalized sup Augmented Dickey-Fuller (GSADF) model and explain the reasons for bubble formations according to the stage of China's economic development and policies implementation. At this stage, considering the differences in economic development levels of different cities, the real estate bubbles in the first, second and third tier cities and the whole country were innovatively identified. Second, on the basis of the DCG-GARCH-CoVaR model to measure the systemic risk of listed financial institutions in China and to classify institutions, the results show that the main source of such risk is the banking sector. Furthermore, by constructing regression models, stock market bubbles and real estate bubbles both positively correlate with systemic risk throughout the sample period. Meanwhile, the impact of bubbles on the systemic risk of different types of financial institutions was taken into account so that regulators prioritized different types of institutions with different characteristics when faced with decisions. Finally, we provide macro-prudential policy advice to regulators in order to weaken the impact of bubbles on financial stability to avoid systemic crises.