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Systemic Risk on Trade Credit Systems: with the Tangible Interconnectedness

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Abstract

Using the unique data set of all the transactions with trade credit via 17 major banks in Korea during 2008–2012, we investigate the relationship between the network structure and the contagion of failures in the financial network. Due to the difference of terms between two types of trade credits in Korea, Account Receivable Financing and Supplier Loan, a non-trivial directed network of banks and firms can be formulated. With respect to this trade credit network (TCN), we propose a measure for systemic risk on liquidity channels by calculating the monthly potential risk of liquidity shortage in the worst-case scenarios and show some comparisons with the basic network characteristics. Results claim that the PageRank centralities of individual banks have significant positive impact on the level of systemic risk. More on, degrees of nodes in TCN follows power-law distributions and the network heterogeneity of given month also has significant positive impact on the risk level: that is, the high degree of imbalance in the liquidity channel implies the severe systemic shortage of the liquidity in the worst-case.

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Notes

  1. This centrality is suggested by Page et al. (1998) and widely used in the network analysis.

  2. A common shock magnifies the correlation between financial conditions of trade creditor and trade debtor. And credit debtors having bad credits tend to match with the trade creditors which have bad credit ratings also. More on, the default of trade creditor may cause the debtors to fail by contracting trade credit supply.

  3. According to our data set, 60.12% (6,612,839 contracts among 11,000,189 cases) are classified as risky. Since firms use installment payments, only 0.95% (104,491 cases) of the risky TCs have full bank balances, which means that the debtor never repays the debt.

  4. We can deduce this statement indirectly from our data. On average, 90.3% of the contract pairs (buyer-seller pairs) reappear in the data. This means that the most of remaining debts are handled within the on-going partnerships.

  5. A power-law distribution has a density function of following form: \({p(x)=\frac{\alpha -1}{x_{min}}\big (\frac{x}{x_{min}}\big )^{-\alpha }}\) The parameter \(\alpha \) is called a scale parameter and represents the slope of density function in a semi-log graph.

  6. The number of firms which are categorized into the statutorily audited firm in the total data is 11,520. And the number of the other firms are 361,086. However, the number of contracts related to the former type of firms is 3,788,280, while the number of contracts not related to them is 7,211,909.

  7. Clauset et al. (2009) provided the open access to their Matlab codes for power-law test. Log-likelihood ratio tests were conducted and results were significant. For lack of space, test results are omitted.

  8. Chi-square statistics of Hausman test for the Eq. (1) was 9.75 and p-value was 0.0076.

  9. Chi-square statistics of Hausman test for the Eq. (2) was 3.55 and p-value was 0.1694.

  10. Chi-square statistics of Hausman test for the Eq. (3) was 0.02 and p-value was 0.8875.

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Acknowledgements

This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government(2014S1A3A2044459). We thank Dain Jung from KAIST who helped us with the data preprocessing and two anonymous reviewers who provided insights that greatly assisted the research.

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Authors and Affiliations

  1. School of Business and Technology Management, College of Business, Korea Advanced Institute of Science and Technology (KAIST) Bldg. N22, Gwahak-ro 335, Yuseong-gu, Daejeon, 305-701, Republic of Korea

    Jisang Lee & Duk Hee Lee

  2. Payment and Settlement Systems Department, Bank of Korea, 39, Namdaemun-ro, Jung-gu, Seoul, 100-794, South Korea

    Sung-Guan Yun

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  1. Jisang Lee
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  2. Duk Hee Lee
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Correspondence to Duk Hee Lee.

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Lee, J., Lee, D.H. & Yun, SG. Systemic Risk on Trade Credit Systems: with the Tangible Interconnectedness. Comput Econ 51, 211–226 (2018). https://doi.org/10.1007/s10614-016-9632-x

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