Over the last years, network analysis has become an active topic of research, with numerous applications in macroeconomics and finance. In a nutshell, network analysis is concerned with representing the interconnections of a large panel as a graph: the vertices of the graph represent the variables in the panel, and the presence of an edge between two vertices denotes the presence of some appropriate measure of dependence between the two variables. Dependence can derive from direct exposures or from indirect or common exposures.
From an economic perspective, the interest on networks has been boosted by the research of, inter alia, Acemoglu et al. (2012), which shows that individual entities can have a non-negligible effect on the aggregate behavior of the economy when the system has a high degree of interconnectedness. Especially since the 2008 global financial crisis, the interest in analyzing the role of network structure in transmitting – or dissipating – stress has grown significantly. This work is concerned with the theory and practice of network analysis techniques for applications in finance and economics.
I found this course extremely useful and well done! I learnt a lot and I am looking forward to put into practice the knowledge I acquired.
Participant in the 2021 edition of the course
After having completed this course, you will be able to:
Understand the basic concepts of network theory, including: vertices, edges, network properties, random graphs;
Derive theoretical results about stability in interbank networks;
Model contemporaneous dependence in large panels of time series;
Estimate large dimensional network models (using LASSO estimation);
Identify the reasons for contagion via indirect exposures;
Select the most appropriate tools for the estimation of large network models;
Distinguish between different network structures;
Identify network structures such as hubs and communities;
Use network models in practical policy applications.