Systemic risk is a complex topic, with a large number of variables and constraints. In this thesis we introduce an agent-based network to study the effects of financial shocks on the financial network. The model takes into consideration the repurchase agreement (repo) market and rehypothecation.
We introduce a financial network consisting of financial agents who are connected through direct channels (bilateral contracts) and indirect channels (markets). Each fi- nancial agent has a balance sheet with liquid assets (cash), collateral (bonds, shares), reverse repo assets, fixed assets (loans and mortgages) on the asset side and repo loans, deposits and equities on the liability side. Agents (i.e., banks) need to satisfy constraints on (i) liquidity, which deals with financial shocks, (ii) collateral, related to repo liabil- ities, rehypothecation, and (iii) solvency constraints, ensuring that equity is positive. Liquidity constrain can be broken by a financial shock (e.g., a bank run), while the collateral constraint can be broken by hoarding credit and collateral price reduction. When liquidity and collateral constraints are broken the financial agent will try to fix them through recalling reverse repos and firesale of fixed assets. Banks that fail to fix their constraints by the end of the day will be considered defaulted.
We introduce netting and novation techniques to deal with defaulted banks and lower the stress on the financial markets. In the netting step we lower the exposure of financial agents by removing cycles in the repo liabilities between banks, while in the novation we redistribute the ownership of bilateral contracts and settle any residuals that are left. We also establish that, under certain conditions on the set of defaulted banks, that the novation step is order indifferent.
Different network topologies and balance sheet compositions are tested under several financial shocks to check the robustness of the financial network under our framework. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/30391 |
| Date | 21 November 2024 |
| Creators | Hassan, Chehaitli |
| Contributors | Matheus, Grasselli, Computational Engineering and Science |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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