The financial network channel of monetary policy transmission: an agent-based model
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Título
The financial network channel of monetary policy transmission: an agent-based modelFecha de publicación
2023-01-13Editor
SpringerISSN
1860-711X; 1860-7128Cita bibliográfica
Alexandre, M., Lima, G.T., Riccetti, L. et al. The financial network channel of monetary policy transmission: an agent-based model. J Econ Interact Coord 18, 533–571 (2023). https://doi.org/10.1007/s11403-023-00377-wTipo de documento
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Resumen
The purpose of this paper is to explore the impact of monetary policy shocks on a financial network, which we dub the “financial network channel of monetary policy transmission”. To this aim, we develop a agent-based ... [+]
The purpose of this paper is to explore the impact of monetary policy shocks on a financial network, which we dub the “financial network channel of monetary policy transmission”. To this aim, we develop a agent-based model (ABM) in which banks extend loans to firms. The resulting bank–firm credit network is structured as determined by plausible behavioral assumptions, with both firms and banks being always willing to close a credit deal with the network partner perceived to be less risky. As our ABM succeeds in reproducing several key stylized facts of bank–firm credit networks, we then assess through simulations how exogenous shocks to the policy interest rate affect some key topological measures of the bank–firm credit network (density, assortativity, size of largest component, and degree distribution). Our simulations show that such topological features of the bank–firm credit network are significantly affected by shocks to the policy interest rate, with such an impact varying quantitatively and qualitatively with the sign, magnitude, and duration of the shocks. [-]
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Journal of Economic Interaction and Coordination, volume 18, pages533–571 (2023)Entidad financiadora
Universitat Jaume I | National Council of Scientific and Technological Development (CNPq - Brazil)
Código del proyecto o subvención
UJI-B2020-16 and AICO/2021/005 | grant 311811/2018-3
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