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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2402.08071 |
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| _version_ | 1866910341371265024 |
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| author | Ugwu, Sunday Akukodi |
| author_facet | Ugwu, Sunday Akukodi |
| contents | This mini-project models propagation of shocks, in time point, through links in connected banks. In particular, financial network of 100 banks out of which 15 are shocked to default (that is, 85.00% of the banks are solvent) is modelled using Erdos and Renyi network -- directed, weighted and randomly generated network. Shocking some banks in a financial network implies removing their assets and redistributing their liabilities to other connected ones in the network. The banks are nodes and two ranges of probability values determine tendency of having a link between a pair of banks. Our major finding shows that the ranges of probability values and banks' percentage solvency have positive correlation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08071 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Contagion on Financial Networks: An Introduction Ugwu, Sunday Akukodi Statistical Finance This mini-project models propagation of shocks, in time point, through links in connected banks. In particular, financial network of 100 banks out of which 15 are shocked to default (that is, 85.00% of the banks are solvent) is modelled using Erdos and Renyi network -- directed, weighted and randomly generated network. Shocking some banks in a financial network implies removing their assets and redistributing their liabilities to other connected ones in the network. The banks are nodes and two ranges of probability values determine tendency of having a link between a pair of banks. Our major finding shows that the ranges of probability values and banks' percentage solvency have positive correlation. |
| title | Contagion on Financial Networks: An Introduction |
| topic | Statistical Finance |
| url | https://arxiv.org/abs/2402.08071 |