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| A Study of Risk Contagion Based on the Interaction Between Common Shocks and Idiosyncratic Risks: Evidence From the Simulation of Listed Banks in China |
| XU Guoxiang, WU Ting, WANG Ying
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School of Statistics and Management/Research Center for Applied Statistics, Shanghai University of Finance and Economics;
School of Insurance, Shanghai Lixin University of Accounting and Finance;
Alibaba Group |
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Abstract “Systemic risk” is a widely used term that is difficult to define and quantify.The emergence and evolution of systemic risk resembles a “black box” with the process controlled by exogenous or endogenous shocks and where the contagion channels and orders dominated by various factors.To simplify the combination of factors involved, academics usually focus on three kinds of risk: internal contagion of idiosyncratic risk, extensive damage caused by external common shocks, and imbalance caused by the accumulation of risk over time.These three forms of risk are not exclusive, and usually occur simultaneously and interact with each other in real crises.
The key to identifying systemic risk events is to estimate the potential “system state” and evaluate the ability of these events to further change the “system state.” The vulnerability of the entire system caused by external shocks determines the depth and breadth of idiosyncratic risk contagion.Thus, the results of this paper shed light on the effects of common external shocks on the banking system and establish a simplified interactive contagion model for common shocks and idiosyncratic risks.We introduce a systemic risk contagion model with three stages: original shocks, incremental shocks, and default shocks, based on their dominant risk factors.The model depicts the dynamic and interactive contagion process of common shocks and idiosyncratic risks, the effects of which spill over into the financial market and inter-bank debt chain.
The current paper has three main findings.First, losses caused by common shocks are much greater than those caused by idiosyncratic risks.Second, the key factors that determine the infection multiplier are size and network relevance, and network relevance has a stronger role in determining the infection multiplier when the size factor is not prominent.For example, it is often the case that small-scale and highly related banks have a higher infection multiplier.Third, the leverage that causes systemic loss is generally enhanced when the loss rate of the risk assets of the bank is between 10% and 25%.Furthermore, regulatory intervention by the government can effectively reduce the systemic risk.
This study makes the following contributions.First, we deconstruct common external shocks.We assume that there is a bank present to bear the external shock (called the “trigger bank”).This bank's assets loss risk spills over in ordered layers throughout the banking network.The microcosmic simulation of common external shocks is helpful to depict the sensitivity of the systemic loss caused by the assets loss of a single bank (called the “infection multiplier”).As such, this finding will help to inform early warning systems and interventions in cases of systemic risk.Second, we build direct and indirect networks based on risk correlations.To distinguish the transmission paths of common risks and idiosyncratic risks, we construct a relationship network for common risk exposure and idiosyncratic risk exposure.The default order of banks is decided dynamically by mutual verification between the direct debt network of the interbank market and the indirect relational networks of common shock contagion models.Compared with the risk superposition model, this model not only better captures each stage of risk contagion, it also does not rely on the historical default data of banks.Third, we measure the systemic risk caused by different trigger banks.Compared with the leave-one-out method, which evaluates losses by directly removing a single bank one at a time, the trigger bank in this study remains in the simulation network until the solvency bankruptcy occurs.This more closely reflects a real risk scenario.
The conclusions of this study provide empirical evidence that will help to make regulatory decisions in the context of systemic risk.First, the results suggest that the assessment of common risk exposure in the banking system should be increased to control systemic risk from the source, and attention should be paid to the identification and evaluation of the network correlation of small and medium-sized banks.Second, in view of the inflection point, a phenomenon in which an infection multiplier triggers banks, we suggest that regulators intensify the blocking intervention mechanism against systemic risk, for example by setting an early warning rate of risk-weighted asset loss to prevent risk escalation.
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Received: 25 March 2019
Published: 02 May 2021
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