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dc.contributor.authorAvakian, Adam J.en_US
dc.date.accessioned2017-09-27T15:52:41Z
dc.date.available2017-09-27T15:52:41Z
dc.date.issued2017
dc.identifier.urihttps://hdl.handle.net/2144/24072
dc.description.abstractModern financial networks are complicated structures that can contain multiple types of nodes and connections between those nodes. Banks, governments and even individual people weave into an intricate network of debt, risk correlations and many other forms of interconnectedness. We explore multiple types of financial network models with a focus on understanding the dynamics and causes of cascading failures in such systems. In particular, we apply real-world data from multiple sources to these models to better understand real-world financial networks. We use the results of the Federal Reserve "Banking Organization Systemic Risk Report" (FR Y-15), which surveys the largest US banks on their level of interconnectedness, to find relationships between various measures of network connectivity and systemic risk in the US financial sector. This network model is then stress-tested under a number of scenarios to determine systemic risks inherent in the various network structures. We also use detailed historical balance sheet data from the Venezuelan banking system to build a bipartite network model and find relationships between the changing network structure over time and the response of the system to various shocks. We find that the relationship between interconnectedness and systemic risk is highly dependent on the system and model but that it is always a significant one. These models are useful tools that add value to regulators in creating new measurements of systemic risk in financial networks. These models could be used as macroprudential tools for monitoring the health of the entire banking system as a whole rather than only of individual banks.en_US
dc.language.isoen_US
dc.subjectPhysicsen_US
dc.subjectBanksen_US
dc.subjectEconophysicsen_US
dc.subjectNetworksen_US
dc.titleDynamic modeling of systemic risk in financial networksen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2017-07-31T22:12:28Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplinePhysicsen_US
etd.degree.grantorBoston Universityen_US


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