Giuseppe Calafiore, Giulia Fracastoro, Anton Proskurnikov
{"title":"金融网络中的违约弹性和最坏情况效应","authors":"Giuseppe Calafiore, Giulia Fracastoro, Anton Proskurnikov","doi":"arxiv-2403.10631","DOIUrl":null,"url":null,"abstract":"In this paper we analyze the resilience of a network of banks to joint price\nfluctuations of the external assets in which they have shared exposures, and\nevaluate the worst-case effects of the possible default contagion. Indeed, when\nthe prices of certain external assets either decrease or increase, all banks\nexposed to them experience varying degrees of simultaneous shocks to their\nbalance sheets. These coordinated and structured shocks have the potential to\nexacerbate the likelihood of defaults. In this context, we introduce first a\nconcept of {default resilience margin}, $\\epsilon^*$, i.e., the maximum\namplitude of asset prices fluctuations that the network can tolerate without\ngenerating defaults. Such threshold value is computed by considering two\ndifferent measures of price fluctuations, one based on the maximum individual\nvariation of each asset, and the other based on the sum of all the asset's\nabsolute variations. For any price perturbation having amplitude no larger than\n$\\epsilon^*$, the network absorbs the shocks remaining default free. When the\nperturbation amplitude goes beyond $\\epsilon^*$, however, defaults may occur.\nIn this case we find the worst-case systemic loss, that is, the total unpaid\ndebt under the most severe price variation of given magnitude. Computation of\nboth the threshold level $\\epsilon^*$ and of the worst-case loss and of a\ncorresponding worst-case asset price scenario, amounts to solving suitable\nlinear programming problems.}","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"30 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Default Resilience and Worst-Case Effects in Financial Networks\",\"authors\":\"Giuseppe Calafiore, Giulia Fracastoro, Anton Proskurnikov\",\"doi\":\"arxiv-2403.10631\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we analyze the resilience of a network of banks to joint price\\nfluctuations of the external assets in which they have shared exposures, and\\nevaluate the worst-case effects of the possible default contagion. Indeed, when\\nthe prices of certain external assets either decrease or increase, all banks\\nexposed to them experience varying degrees of simultaneous shocks to their\\nbalance sheets. These coordinated and structured shocks have the potential to\\nexacerbate the likelihood of defaults. In this context, we introduce first a\\nconcept of {default resilience margin}, $\\\\epsilon^*$, i.e., the maximum\\namplitude of asset prices fluctuations that the network can tolerate without\\ngenerating defaults. Such threshold value is computed by considering two\\ndifferent measures of price fluctuations, one based on the maximum individual\\nvariation of each asset, and the other based on the sum of all the asset's\\nabsolute variations. For any price perturbation having amplitude no larger than\\n$\\\\epsilon^*$, the network absorbs the shocks remaining default free. When the\\nperturbation amplitude goes beyond $\\\\epsilon^*$, however, defaults may occur.\\nIn this case we find the worst-case systemic loss, that is, the total unpaid\\ndebt under the most severe price variation of given magnitude. Computation of\\nboth the threshold level $\\\\epsilon^*$ and of the worst-case loss and of a\\ncorresponding worst-case asset price scenario, amounts to solving suitable\\nlinear programming problems.}\",\"PeriodicalId\":501128,\"journal\":{\"name\":\"arXiv - QuantFin - Risk Management\",\"volume\":\"30 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuantFin - Risk Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2403.10631\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Risk Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2403.10631","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Default Resilience and Worst-Case Effects in Financial Networks
In this paper we analyze the resilience of a network of banks to joint price
fluctuations of the external assets in which they have shared exposures, and
evaluate the worst-case effects of the possible default contagion. Indeed, when
the prices of certain external assets either decrease or increase, all banks
exposed to them experience varying degrees of simultaneous shocks to their
balance sheets. These coordinated and structured shocks have the potential to
exacerbate the likelihood of defaults. In this context, we introduce first a
concept of {default resilience margin}, $\epsilon^*$, i.e., the maximum
amplitude of asset prices fluctuations that the network can tolerate without
generating defaults. Such threshold value is computed by considering two
different measures of price fluctuations, one based on the maximum individual
variation of each asset, and the other based on the sum of all the asset's
absolute variations. For any price perturbation having amplitude no larger than
$\epsilon^*$, the network absorbs the shocks remaining default free. When the
perturbation amplitude goes beyond $\epsilon^*$, however, defaults may occur.
In this case we find the worst-case systemic loss, that is, the total unpaid
debt under the most severe price variation of given magnitude. Computation of
both the threshold level $\epsilon^*$ and of the worst-case loss and of a
corresponding worst-case asset price scenario, amounts to solving suitable
linear programming problems.}