Paper 1: 'Statistical clustering of temporal networks through a dynamic stochastic block model’ by Matias & Miele. It was published in Series B, Volume 79, Issue 4, September 2017

Presented by Catherine Matias, CNRS, Sorbonne Université and Université de Paris

Abstract
Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and frequentist inference in a model that combines a stochastic block model for its static part with independent Markov chains for the evolution of the nodes groups through time. We model binary data as well as weighted dynamic random graphs (with discrete or continuous edges values). Our approach, motivated by the importance of controlling for label switching issues across the different time steps, focuses on detecting groups characterized by a stable within‐group connectivity behaviour. We study identifiability of the model parameters and propose an inference procedure based on a variational expectation–maximization algorithm as well as a model selection criterion to select the number of groups. We carefully discuss our initialization strategy which plays an important role in the method and we compare our procedure with existing procedures on synthetic data sets. We also illustrate our approach on dynamic contact networks: one of encounters between high school students and two others on animal interactions. An implementation of the method is available as an R package called dynsbm.

Paper 2:         'A network analysis of the volatility of high dimensional financial series' by Barigozzi & Hallin. It was published in Series C, Volume 66, Issue in April 2017

Presented by Matteo Barigozzi, University of Bologna, Italy

Abstract
Interconnectedness between stocks and firms plays a crucial role in the volatility contagion phenomena that characterize financial crises, and graphs are a natural tool in their analysis. We propose graphical methods for an analysis of volatility interconnections in the Standard & Poor's 100 data set during the period 2000–2013, which contains the 2007–2008 Great Financial Crisis. The challenges are twofold: first, volatilities are not directly observed and must be extracted from time series of stock returns; second, the observed series, with about 100 stocks, is high dimensional, and curse‐of‐dimensionality problems are to be faced. To overcome this double challenge, we propose a dynamic factor model methodology, decomposing the panel into a factor‐driven and an idiosyncratic component modelled as a sparse vector auto‐regressive model. The inversion of this auto‐regression, along with suitable identification constraints, produces networks in which, for a given horizon h, the weight associated with edge (i,j) represents the h‐step‐ahead forecast error variance of variable i accounted for by variable j's innovations. Then, we show how those graphs yield an assessment of how systemic each firm is. They also demonstrate the prominent role of financial firms as sources of contagion during the 2007–2008 crisis.

Chair: Yi Yu, University of Warwick

Discussant: Ernst Wit, University of Groningen, The Netherlands

Both papers will be available to download free-of-charge from two weeks before the webinar.