The network images presented in this website are computed from the forecast error variance decompositions of a set of vector autoregressive (VAR) models estimated on a rolling sample basis.

We employ a rolling sample of 250 trading days over the period January 2nd 2006 to July 27th 2015. There are 2,246 rolling samples in total.

For each rolling sample, we estimate a VAR model which approximates the dynamics of a vector of sovereign CDS spreads (the main object of interest), sovereign bond yields (to control for variations in the domestic conditions between countries) and global controls (to account for sources of common variation).

The rolling sample VAR models are estimated by system least squares in MATLAB.

For each rolling sample, we compute the 5-days-ahead generalised forecast error variance decomposition of the VAR model. This is then transformed by a simple normalisation to yield the adjacency matrix summarising the bilateral relations among the variables in the model.

When we plot graphs, the dates that are reported correspond to the last day of each rolling sample.

A rigorous technical discussion of our estimation framework can be found in the paper.