Credit default swap spread and succession events

2007 ◽  
Vol 15 (4) ◽  
pp. 450-463
Author(s):  
Halit Gonenc ◽  
Floris Schorer ◽  
Willem P.F. Appel
2020 ◽  
pp. 097215091988617
Author(s):  
Alessandra Ortolano ◽  
Eliana Angelini

The article analyses banks’ credit default swap (CDS) spread determinants, in light of the Eurozone debt crisis. The attention to this aspect is due to the very linkage between banking and sovereign sectors particularly evident during the aforementioned crisis. The study is conducted on a sample of Eurozone banks over the period 2009–2014 through a feasible generalized least squares (FGLS) linear panel data regression. The variables adopted are both balance sheet ratios and macroeconomic factors. The main results confirm the attention pointed at the influence of public conditions to the banking sector, as proved by the significance of variables like the 10-year bond yields or the long-term sovereign rating. It is also interesting to observe the output dealing with public debt, which may suggest opportunities not only of investment but also of speculation for banks on the debt itself. Balance sheet ratios, instead, are not significant. Our study is an additional contribution to the strand of literature that analyses the strong interconnections between the riskiness of banks and the public sector and it is a suggestion to proceed the research with a deeper analysis on systemic risk and its impact on banking CDS spread.


2017 ◽  
Vol 52 (1) ◽  
pp. 243-275 ◽  
Author(s):  
Mike Anderson

This paper documents an increase in the comovement between credit default swap (CDS) spread changes during the 2007–2009 crisis and investigates the source of that increase. One possible explanation is that comovement increased because fundamental values became more correlated. However, I find that changes in fundamentals account for only 23% of the increase in covariance. The remaining increase is attributed to changes in liquidity and the market price of default risk. In contrast, counterparty risk played an insignificant role. Although both contributed, the increase in covariance was driven more by variation in exposures than factor variance–covariance.


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