Integrating granular data into a multilayer network: an interbank model of the euro area for systemic risk assessment

Battle Count: 62

The paper is primarily focused on systemic risk assessment and banking supervision rather than trading strategies. However, understanding interbank network structure, contagion channels, and systemic importance of institutions could inform risk management for trading desks, counterparty risk assessment, and understanding market-wide stress scenarios. The fire-sale dynamics and liquidity hoarding mechanisms are relevant for understanding market microstructure during stress periods.

High complexity due to: (1) integration of 8 heterogeneous granular datasets with different frequencies, definitions, and reporting structures; (2) entity identification and consolidation across multiple regulatory frameworks; (3) construction of 5+ distinct network layers with different edge semantics (directed/undirected, nominal/market value); (4) implementation of DebtRank with multiple calibrations; (5) agent-based model with fixed-point problems for short-term market clearing and securities market clearing; (6) requires distributed computing infrastructure (Hadoop/Spark) for data processing; (7) parameter calibration for risk weights, price impact, and liquidity buffers.

The paper relies on ECB supervisory and statistical datasets (AnaCredit, CSDB, SHSG, SFTDS, RIAD, ROSSI, COREP, FINREP) which are not publicly available. The methodology is well-documented with detailed equations and algorithm descriptions, but the data integration pipeline (Hadoop/Spark processing) and specific parameter calibrations (risk weights, price impact parameters) limit full reproducibility. The snapshot date (June 2021) and consolidation rules are specified.