Extracting the sovereigns� CDS market hierarchy: a correlation-filtering approach

Since correlation may be interpreted as a measure of the influence across time-series, it may be conveniently mapped into a distance and into a weighted adjacency matrix. Based on such matrix, network theory has attempted to filter out the noise in correlation matrices by extracting the dominant hierarchy (i.e. the strongest linear-dependence signals) within time-series. The aim of this brief paper is to find the current hierarchy in the sovereigns� CDS market after the structural shift caused by the failure of Lehman Brothers. Thus, based on two different correlation-into-distance mapping techniques and a minimal spanning tree-based correlation-filtering methodology on 36 sovereign CDS spread time-series, the target is to identify which sovereigns are providing the strongest -less noisy- and most informative signals. The resulting sovereigns� CDS market hierarchy agrees with prior findings of Gilmore et al. (2010) regarding sovereigns� bonds market, such as the importance of geographical clustering and the idiosyncratic nature of Japan and United States. Additionally, results (i) confirm that a small set of common factors affect the entire system; (ii) identify the relevance of credit rating clustering; (iii) identify Russia, Turkey and Brazil as regional benchmarks; (iv) suggest that lower-medium grade rated sovereigns are the most influential, but also the most prone to contagion; and (v) suggest the existence of a Latin American common factor". "