Extracting the Sovereigns’ CDS Market Hierarchy: A Correlation-filtering Approach
La serie Borradores de Economía es una publicación de la Subgerencia de Estudios Económicos del Banco de la República. Los trabajos son de carácter provisional, las opiniones y posibles errores son responsabilidad exclusiva del autor y sus contenidos no comprometen al Banco de la República ni a su Junta Directiva.
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”.
The opinions and statements are the sole responsibility of the authors and do not necessarily represent neither those of Banco de la República nor of its Board of Directors. Results are illustrative; they may not be used to infer credit quality or to make any type of assessment. As usual, any remaining errors are the authors’ own.
