{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T11:39:16Z","timestamp":1767008356475,"version":"3.37.3"},"reference-count":60,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T00:00:00Z","timestamp":1604016000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71871115","71501094","71501131","2019SJZDA035","2017ZSTD005"],"award-info":[{"award-number":["71871115","71501094","71501131","2019SJZDA035","2017ZSTD005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018529","name":"Major Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province","doi-asserted-by":"crossref","award":["71871115","71501094","71501131","2019SJZDA035","2017ZSTD005"],"award-info":[{"award-number":["71871115","71501094","71501131","2019SJZDA035","2017ZSTD005"]}],"id":[{"id":"10.13039\/501100018529","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Outstanding Innovation Team of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province","award":["71871115","71501094","71501131","2019SJZDA035","2017ZSTD005"],"award-info":[{"award-number":["71871115","71501094","71501131","2019SJZDA035","2017ZSTD005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2020,10,30]]},"abstract":"<jats:p>Banks and enterprises constitute a multilayered, multiattribute, multicriteria credit-related super network due to financial transaction behaviors, such as credit, wealth management, savings, and derivatives. Such a network has become an important channel for credit risk cross-contagion. This study constructs a two-layer network model of credit risk contagion between the bank and corporate counterparties from the perspective that banks do not withdraw loans from enterprises by considering the influence of corporate credit defaults on their counterparties under the credit linkage. This study analyzes the mechanism of influencing the evolution of bank-enterprise counterparty credit risk contagion in the two-tier network through theoretical analysis, including the following: the enterprises\u2019 coping ability, risk preference, influence, level of interenterprise credit risk contagion and its network heterogeneity in the interenterprise credit association network, the risk prevention and control ability, business correlation degree, interbank credit risk contagion and its network heterogeneity in the interbank credit association network, the level of credit risk contagion between bank-enterprise counterparty credit association networks, and other factors in the case that banks do not withdraw loans from enterprises. In addition, this study performs a calculation experiment to analyze the characteristics of the evolution of counterparty credit risk contagion of bank and corporate counterparties under the double-layer network. The following four major conclusions can be drawn from the results. First, in the interenterprise credit-related network, the threshold of credit risk contagion rate is positively correlated with the marginal increase in risk perception and risk leveling ability of the enterprise. By contrast, such threshold is negatively correlated with the marginal decrease in the initial economic impact, leverage level, and influence of the enterprise. Moreover, the scale of corporate counterparty credit risk contagion is negatively correlated with the enterprise\u2019s risk perception level and risk spillover ability but positively correlated with the enterprise\u2019s initial economic shock level, the enterprise\u2019s leverage level, and influence. Second, in the interbank credit association network, the threshold of the rate of credit risk contagion is negatively correlated with the marginal decrease in the degree of interbank business association but positively correlated with the marginal increase in the bank\u2019s risk resistance ability and risk information processing ability. Furthermore, the scale of credit risk contagion of bank counterparties is positively correlated with the degree of interbank business association but negatively correlated with the bank\u2019s ability to resist risks and process risk information. Third, if the heterogeneity of the credit-related network of bank-enterprise counterparties is high, then the rate threshold of credit risk contagion is high and the scale of credit risk diffusion is low. Moreover, the scale of credit risk contagion of bank counterparties is positively correlated with the marginal decrease in the degree of corporate and bank counterparties. Finally, the scale of bank counterparty credit risk contagion is a monotonically increasing convex function of the credit risk contagion rate in the enterprise credit association network and among the bank-enterprise networks.<\/jats:p>","DOI":"10.1155\/2020\/3690848","type":"journal-article","created":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T02:50:08Z","timestamp":1604112608000},"page":"1-25","source":"Crossref","is-referenced-by-count":12,"title":["Double-Layer Network Model of Bank-Enterprise Counterparty Credit Risk Contagion"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3013-243X","authenticated-orcid":true,"given":"Tingqiang","family":"Chen","sequence":"first","affiliation":[{"name":"School of Economic and Management, Nanjing Tech University, Nanjing 211816, China"},{"name":"Research Center of Big Data Decision and Performance Evaluation, Nanjing Tech University, Nanjing 211816, China"}]},{"given":"Qinghao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Economic and Management, Nanjing Tech University, Nanjing 211816, China"},{"name":"Research Center of Big Data Decision and Performance Evaluation, Nanjing Tech University, Nanjing 211816, China"}]},{"given":"Yutong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Economic and Management, Nanjing Tech University, Nanjing 211816, China"}]},{"given":"Suyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Stock Exchange, Shanghai 200120, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1142\/s0218127413501174"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jedc.2016.03.002"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2015.08.028"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2015.11.044"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfs.2017.06.009"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1080\/14697688.2016.1274046"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfs.2017.08.006"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.01.018"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1007\/s00181-011-0512-x"},{"issue":"2","key":"10","first-page":"231","article-title":"Lending attitude as a financial accelerator in a credit network economy","volume":"8","author":"D. 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