Systemic Risk and Default Contagion in Financial Networks: Identifying Systemically Important Banks
(2024)Department of Automatic Control
- Abstract
- This thesis uses real data from the Bank of International Settlements to create financial networks for the interbank market using five different methods for network reconstruction. The goal is to analyze how defaults propagate to assess the importance of banks and to examine how the network’s structure affects the system’s vulnerability. By applying network theory, communicability theory, and the DebtRank algorithm, we aim to identify which banks are the most vulnerable and which propagate the largest losses to the system. We also investigate how DebtRank correlates with centrality and communicability measures. Our results will be compared to the Basel Committee’s annual assessment of global systemically important banks.
Our findings... (More) - This thesis uses real data from the Bank of International Settlements to create financial networks for the interbank market using five different methods for network reconstruction. The goal is to analyze how defaults propagate to assess the importance of banks and to examine how the network’s structure affects the system’s vulnerability. By applying network theory, communicability theory, and the DebtRank algorithm, we aim to identify which banks are the most vulnerable and which propagate the largest losses to the system. We also investigate how DebtRank correlates with centrality and communicability measures. Our results will be compared to the Basel Committee’s annual assessment of global systemically important banks.
Our findings show small differences between the network reconstruction methods. The most noticeable difference is that the minimum density method produces more resilient networks when equity is low. In contrast, the small-world method results in networks with slightly higher losses, especially when equity is in the middle range. Additionally, our results indicate that JP Morgan is the most systemically important bank in most scenarios, matching the Basel Committee’s conclusions. However, we believe our methods overestimate the importance of some of the largest Chinese banks. We also show that PgeRank and impact diffusion have the highest correlation with DebtRank impact. Finally, we conclude that Katz centrality and impact susceptibility show a strong correlation with DebtRank vulnerability. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9174167
- author
- Clarke Nilsson, Oscar and Relander, Ossian
- supervisor
-
- Emma Tegling LU
- Giacomo Como LU
- organization
- year
- 2024
- type
- H3 - Professional qualifications (4 Years - )
- subject
- report number
- TFRT-6240
- other publication id
- 0280-5316
- language
- English
- id
- 9174167
- date added to LUP
- 2024-09-10 15:17:47
- date last changed
- 2024-09-10 15:17:47
@misc{9174167,
abstract = {{This thesis uses real data from the Bank of International Settlements to create financial networks for the interbank market using five different methods for network reconstruction. The goal is to analyze how defaults propagate to assess the importance of banks and to examine how the network’s structure affects the system’s vulnerability. By applying network theory, communicability theory, and the DebtRank algorithm, we aim to identify which banks are the most vulnerable and which propagate the largest losses to the system. We also investigate how DebtRank correlates with centrality and communicability measures. Our results will be compared to the Basel Committee’s annual assessment of global systemically important banks.
Our findings show small differences between the network reconstruction methods. The most noticeable difference is that the minimum density method produces more resilient networks when equity is low. In contrast, the small-world method results in networks with slightly higher losses, especially when equity is in the middle range. Additionally, our results indicate that JP Morgan is the most systemically important bank in most scenarios, matching the Basel Committee’s conclusions. However, we believe our methods overestimate the importance of some of the largest Chinese banks. We also show that PgeRank and impact diffusion have the highest correlation with DebtRank impact. Finally, we conclude that Katz centrality and impact susceptibility show a strong correlation with DebtRank vulnerability.}},
author = {{Clarke Nilsson, Oscar and Relander, Ossian}},
language = {{eng}},
note = {{Student Paper}},
title = {{Systemic Risk and Default Contagion in Financial Networks: Identifying Systemically Important Banks}},
year = {{2024}},
}