Time series models for credit default swap premiums

Abstract

We present statistical models for the continuous-time Dynamics of credit Default swap (CDS) premium within an intensity-based credit risk modeling Framework. Based on historical daily CDS Premiums for a large set of different corporate refence entities from several developed countries, we fit continuous-time autoregressive moving-average processes of an appropriate order driven by a Levy process. We recover the driving noise process, which only Shows a stochastic volatility effect for particular branches. On a distributional Level, the increments of the noise process are, as a rule, best modeled by a normal inverse Gaussian distribution.