Detecting abnormal changes in credit default swap spreads using matching-portfolio models

Fabio Bertoni and Stefano Lugo

Post-Print from HAL

Abstract: We evaluate the size and power of different statistical tests and adjustment methods for matching-portfolio models to detect abnormal changes in credit default swap (CDS) spreads. The sign-test generally dominates the signed-rank test in terms of size, and dominates both the t-test and the signed-rank test in terms of power. Traditional adjustment methods often lead to a misspecified sign-test. We propose a new and parsimonious method (the spread-matched method), which leads to a well-specified and more powerful sign-test. The superiority of the spread-matched method is particularly evident for observations characterized by extreme levels of CDS spread. Analyses of CDS samples differing by contract maturity, data source, and time period confirm these results. We perform an event study on rating downgrades to illustrate how the choice of tests and adjustment methods can affect inference.

Keywords: Event studies; Credit default swaps; Matching-portfolio models; Size and power of tests (search for similar items in EconPapers)
Date: 2018-05-01
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Published in Journal of Banking & Finance, 2018, 90, 146-158 p. ⟨10.1016/j.jbankfin.2018.03.009⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Detecting abnormal changes in credit default swap spreads using matching-portfolio models (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02312138

DOI: 10.1016/j.jbankfin.2018.03.009

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().