{"title":"系统风险和金融网络","authors":"Bingqing Li , Xiaoyuan Zhang","doi":"10.1016/j.qref.2023.12.012","DOIUrl":null,"url":null,"abstract":"<div><p>We develop a network-based probabilistic model to analyze systemic risk within a network of interconnected institutions. Harnessing the power of economic connections, we construct a weighted network that effectively captures the extent of direct risk spillovers. Then the risk contagion probabilistic model is constructed with the aid of the risk orbit contagion idea and inter-institutional dependencies. Our model examines contagion characteristics, uncertainty, and interdependence, revealing that neither a ring nor a complete financial network is optimal. We discover that the expected loss of the network does not have a monotonic relationship with the number of partners, depending on the trade-off between the network density and direct risk spillovers to mitigate systemic risk.</p></div>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Systemic risk and financial networks\",\"authors\":\"Bingqing Li , Xiaoyuan Zhang\",\"doi\":\"10.1016/j.qref.2023.12.012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>We develop a network-based probabilistic model to analyze systemic risk within a network of interconnected institutions. Harnessing the power of economic connections, we construct a weighted network that effectively captures the extent of direct risk spillovers. Then the risk contagion probabilistic model is constructed with the aid of the risk orbit contagion idea and inter-institutional dependencies. Our model examines contagion characteristics, uncertainty, and interdependence, revealing that neither a ring nor a complete financial network is optimal. We discover that the expected loss of the network does not have a monotonic relationship with the number of partners, depending on the trade-off between the network density and direct risk spillovers to mitigate systemic risk.</p></div>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-01-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1062976923001503\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1062976923001503","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
We develop a network-based probabilistic model to analyze systemic risk within a network of interconnected institutions. Harnessing the power of economic connections, we construct a weighted network that effectively captures the extent of direct risk spillovers. Then the risk contagion probabilistic model is constructed with the aid of the risk orbit contagion idea and inter-institutional dependencies. Our model examines contagion characteristics, uncertainty, and interdependence, revealing that neither a ring nor a complete financial network is optimal. We discover that the expected loss of the network does not have a monotonic relationship with the number of partners, depending on the trade-off between the network density and direct risk spillovers to mitigate systemic risk.