Summary:
The economic connections between enterprises result in tightly linked networks. Therefore, a corporation's dishonesty risk is not independent, but has a contagion effect in the surrounding corporate network. A credit guarantee network is an important medium for credit risk contagion. Extensive credit guarantee networks have been established among domestic enterprises. When an enterprise experiences dishonesty risk, the risk spreads along the guarantee network to the surrounding enterprises, which is a complex network dynamics process. However, traditional economic tools cannot effectively analyze this process; risk monitoring and prevention can be more effective through the application of big data and artificial intelligence. Traditional bond credit research, whether it uses a structured or parsimonious model, holds that the credit spread of a corporation depends on its own business conditions, ignoring contagion effects among corporate networks. There have been few studies of the impact of dishonesty contagion on bond credit risk, and the following questions remain unanswered: Are bonds that have not incurred dishonesty risk affected by dishonesty risk contagion from other entities？ If a dishonesty risk contagion effect exists, does it have the same impact on enterprises with different ownership structures？ The guarantee network is a credit lending network among enterprises, and it also represents the contagion path of the dishonesty risk. Dishonesty risk, as a broad credit risk, has a contagion effect in the guarantee network, affecting the pricing of credit bonds. Three types of dishonesty risk contagion effects are identified in this research: a direct effect, a local effect, and a global diffusion effect. Based on 433000 pieces of guarantee data from non-financial enterprises, this paper constructs a corporate credit guarantee network month by month, including 5,578 bond issuers. It also uses data of defaulters disclosed by the Supreme People’s Court and bond default data to identify the dishonest behaviors of enterprises. The empirical results show that the three types of contagion effects have low correlations with each other, and thus they illustrate the effects from different perspectives. Contagion effects will cause the credit spreads of bond issuers to increase significantly. For direct and local contagion effects, the contagion effect of the guarantor has a greater impact on the bond credit spread than that of the guarantee, as the credit qualification of the guarantee is worse than that of the guarantor, and it is more susceptible to direct risk contagion. From the perspective of enterprise ownership, state-owned enterprise bonds are more sensitive to the global diffusion effect, and private enterprise bonds are more responsive to direct contagion effects and local contagion effects. In addition, the dishonesty risk contagion effect will reduce enterprises' refinancing ability. The local contagion effect of the dishonesty risk may cause the issuers' borrowing and financing amounts to decrease in the following year, and the global diffusion effect will lower the bond financing amount in the following year. This paper makes three major contributions to the literature. (1) In terms of research perspective, it reveals the impact of dishonesty contagion on bond credit spreads and supplements the research on credit bond pricing. (2) In terms of research objects, previous research on network risk contagion mainly focused on local contagion effects rather than global diffusion effects. This paper distinguishes three types of contagion effects (direct contagion, local contagion, and global diffusion) and comprehensively reveals the transmission mechanism from the local to the whole, supplementing the research on network risk diffusion. (3) In terms of research methods, most research on network risk contagion examines the correlation of credit risk and network structure to test the network contagion characteristics of risk, ignoring the network heterogeneity of risk diffusion. When constructing risk contagion variables, this paper takes into account heterogeneous factors such as network structure characteristics, the location of both enterprises, and the risk sources in the network, supplementing the research on network risk.
王雷, 李晓腾, 张自力, 赵学军. 失信风险传染会影响债券定价吗？——基于担保网络大数据的实证研究[J]. 金融研究, 2022, 505(7): 171-189.
WANG Lei, LI Xiaoteng, ZHANG Zili, ZHAO Xuejun. Does Dishonesty Risk Contagion Affect Bond Pricing？ An Empirical Study Based on Guarantee Network Big Data. Journal of Financial Research, 2022, 505(7): 171-189.
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