The Leaders, the Laggers, and the “Vulnerables”

Abstract

We examine the lead-lag effect between the large and the small capitalization financial institutions by constructing two global weekly rebalanced indices. We focus on the 10% of stocks that “survived” all the rebalancings by remaining constituents of the indices. We sort them according to their systemic importance using the marginal expected shortfall (MES), which measures the individual institutions’ vulnerability over the market, the network based MES, which captures the vulnerability of the risks generated by institutions’ interrelations, and the Bayesian network based MES, which takes into account different network structures among institutions’ interrelations. We also check if the lead-lag effect holds in terms of systemic risk implying systemic risk transmission from the large to the small capitalization, concluding a mixed behavior compared to the index returns. Additionally, we find that all the systemic risk indicators increase their magnitude during the financial crisis.

Risk Measurement of Local Government Debt Based on Complex Networks: Taking China’s Urban Investment Bonds as an Example

“…Once a risk occurs, it will be transformed into a local government debt risk, which will then spill over to the higher-level and lower-level governments, making local debt a central government debt, which ultimately leads to systemic risks. Due to the interconnectedness of regional economies, there are risk linkages and "systemically important nodes" in the urban investment bond network [2,3].…”

Effectiveness of Price Limit on Stock Market Network: A Time‐Migrated DCCA Approach

“…However, it should be further noticed that the price series have not only synchronous relationships but also asynchronous relationships in real-time stock markets. For example, there is a lead-lag effect on the stock market, which means that stock prices of some firms show a delayed or ahead temporal evolution pattern to other firms' stock prices [27][28][29][30]. Since a possible delay between the stocks could be accounted in the time series, we consider the following case in Figure 2(b): supposing two time series ξ(t) and η(t) in the stock market, we calculate the detrended correlations of each segment i in ξ(t) and η(t), but in some cases, the segment i of ξ(t) may have a relationship with segment j of η(t) in some cases.…”