GREDEG Economics Journal Club

Francesco Toni and Christian Vezil are glad to invite you to the second meeting of the Journal Club: “Financial networks and reconstruction techniques”.
 

The meeting will take place on Thursday 12th December, at 14:00 in the Picasso Room

The session will consist of a 30-minute presentation, followed by a 30-minute open discussion. Please note that this session will be held exclusively in person.
PhD students in Economics will be at the heart of this initiative, presenting papers that have significantly influenced their research areas. We encourage presentations on all Economics fields, including but not limited to Econometric Methods, Environmental Economics, Industrial Dynamics, Innovation, and Macroeconomics. While PhD students will lead the presentations, we warmly invite faculty members to attend and contribute their valuable expertise and insights to the discussions.

For this session, Matteo Orlandini will present the paper “Econometric measures of connectedness and systemic risk in the finance and insurance sectors” by Monica Billio, Mila Getmansky, Andrew Lo, and Loriana Pelizzon, where an application of a network reconstruction methodology is presented (abstract 1 below).
As an additional reading, we will discuss the paper “NETS: Network estimation for time series” by Matteo Barigozzi and Christian Brownlees (abstract 2 below).
  ABSTRACT 1: “Econometric measures of connectedness and systemic risk in the finance and insurance sectors”
We propose several econometric measures of connectedness based on principal-components analysis and Granger-causality networks, and apply them to the monthly returns of hedge funds, banks, broker/dealers, and insurance companies. We find that all four sectors have become highly interrelated over the past decade, likely increasing the level of systemic risk in the finance and insurance industries through a complex and time-varying network of relationships. These measures can also identify and quantify financial crisis periods, and seem to contain predictive power in out-of-sample tests. Our results show an asymmetry in the degree of connectedness among the four sectors, with banks playing a much more important role in transmitting shocks than other financial institutions.

ABSTRACT 2: “NETS: Network estimation for time series”
We model a large panel of time series as a vector autoregression where the autoregressive matrices and the inverse covariance matrix of the system innovations are assumed to be sparse. The system has a network representation in terms of a directed graph representing predictive Granger relations and an undirected graph representing contemporaneous partial correlations. A LASSO algorithm called NETS is introduced to estimate the model. We apply the methodology to analyze a panel of volatility measures of 90 blue chips. The model captures an important fraction of total variability, on top of what is explained by volatility factors, and improves out-of-sample forecasting.