Agenda

Guan Yun Kenwin Maung (Rochester University) -  Large Networks Autoregressions with Unknown Adjacency Matrix

Abstract:  
Many network econometric models rely on known adjacency matrices. This becomes a problem for investigations when the network structure is not readily accessed or constructed such as those typically observed in macroeconomics and finance. Furthermore, direct estimation may be cumbersome or infeasible if the number of units in the network is large. To deal with this, we propose a Structural Vector Autoregression (SVAR) data-driven approach to recover the network structure via matrix regression under a large N and T asymptotic framework. The high-dimensionality of the problem is dealt with by focusing on low-rank representations of the network - hub and authority centralities. We show, both theoretically and through simulations, that the reduced-form estimator is consistent and asymptotically normal, and suggest an identification strategy for the SVAR as implied by its network structure. In our empirical study, we extract volatility connectedness between major US financial institutions and find a greater degree of interconnectedness compared to Diebold and Yilmaz (2014, 2015). We further demonstrate the utility of the estimated network for systemic risk analysis by identifying key propagators of volatility spillovers in the financial sector.

Link zoom: unive.zoom.us/j/87511181664

ID riunione: 875 1118 1664