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BIMSA Digital Economy Lab Seminar
Inferring the Global Currency Network Based on Deep Graph Learning
Inferring the Global Currency Network Based on Deep Graph Learning
演讲者
时间
2026年03月06日 15:00 至 16:00
地点
A3-2-303
线上
Zoom 435 529 7909
(BIMSA)
摘要
Existing studies fail to trace out global currency network owing to the limited public data. This study proposes a novel deep learning model—Attentive Spatial-Temporal Hyperbolic Graph Convolutional Network (AST-HGCN)—to infer bilateral preferences for the USD and EUR using cross-border banking flows from the BIS. The AST-HGCN employs a sandwich structure of two temporal and one spatial module, integrating currency inertia and cross-border capital flows. The spatial module leverages Hyperbolic Graph Convolution to capture the core-periphery and small-world properties of global currency networks. Our model outperforms Euclidean-based methods, improving network structural fidelity by 13.9% and forecast accuracy by 9.5%. We empirically investigate the inferred USD and EUR networks from 2002–2022. Results show that the inferred preferences closely approximate available snapshots of USD- and EUR-denominated cross-border bank loans. The dynamic evolution of USD and EUR preferences diverges across income groups and varies with the global financial cycle. Moreover, both networks exhibit core-periphery and small-world structures that amplify liquidity shocks and systemic risk propagation. This suggests that our model innovatively captures the strategic complementarity between borrower and lender currency choices through core-node dominance and regional preference convergence.
演讲者介绍
Ji Qi is a postdoc at BIMSA & Tsinghua University. Her research focuses on the digital economy, graph neural network, and knowledge graph.