Tail Return Analysis of Bear Stearns Credit Default Swaps

Testing for Nonlinear Dependence in the Credit Default Swap Market

“…In conclusion, as 85% of the underlying assets in CDS contracts are 5-year bonds [7], it is surprising to note that the GARCH residuals for the bond sample appear to be iid, while the CDS sample GARCH residuals indicate evidence of nonlinear dependence. This may indicate that the source of the nonlinear dependence is particular to the CDS contract and does not relate to the assets underlying the contract.…”

“…The results are mixed with some evidence of nonlinear dependencies remaining in the data. For an example of the analysis of the CDS GARCH residuals, see Li and Mizrach [7]. Li and Mizrach [7] apply a univariate model to CDS data using the GARCH model but do not test the residuals for iid characteristics.…”

“…In general, the existing literature often places the CDS in a dependency model and analyses inter-asset or crossasset dependencies such as correlations, [8][9][10][11][12]. The copula function is one such model; this model assumes that after a transformation, the variables under analysis follow a multivariate probability distribution, most commonly the Gaussian [13], the Student t, the Gumbel [14,15], or the exponential probability distribution [7]. Should the marginal distribution of the CDS data be shown to be significantly nonlinear and non-iid, even after the application of a GARCH model, we can no longer assume that the data can be described by a probability distribution function without further modeling.…”