The research agenda developed and performed during the MultiNetMetrics project focused on novel methodologies for the extraction and the analysis of network in finance and macroeconomics, but also for a general use. Firstly, the project propose an innovative Bayesian nonparametric (BNP) time-varying graphical framework for making inference in high-dimensional time series. In particular, two different prior distributions for the transition matrix of a vector Autoregressive (VAR) model have been considered. The first is based on spike-and-slab prior with Dirichlet Process Mixture priors on the slab component and the second one on a time-series Dirichlet Process Prior. The proposed BNP time-varying VAR model allows to infer time-varying Granger causality networks from time series; to flexible model and cluster non-zero time-varying coefficients and to accomodate for potential non-linearities. The nice feature of this part of the MultiNetMetrics project is the possibility to work with both observed and latent networks, where in the second case, the network extraction is estimate a posteriori. Secondly, MultiNetMetrics deal with multilayer and multiplex networks and it can be related to the first part. As a second theoretical contribution, we deal with a Bayesian version of the Generalized Autoregressive Score (GAS) model, where the estimation is done via a Bayesian algorithm and not in a frequentist way. In this scenario, the methodology has been applied to CDS spread data from around the world and we deal with a Vector Autoregressive (VAR) model with GAS structure of the error matrix. The proposed methodology and algorithm has been used to study a posterior the network structure of the linkages between different CDS spread across countries and has been developed by using a two-step approach. The first step deals with the estimation of the model, while the second step works with a Signal adaptive Variable selector (SAVS) for the network extraction. In conclusion, the project has worked with different Bayesian techniques with a common denominator as the network extraction. In the project, all the codes have been self-written in MATLAB with the opportunity of using parallelization. The MultiNetMetrics website has been regularly updated with informations regarding publications, working papers and conference participations.
