Journal of Credit Risk

Risk.net

On recovery and intensity's correlation: a new class of credit risk models

Raquel M. Gaspar, Irina Slinko

ABSTRACT

Empirical literature increasingly supports that both the probability of default (PD) and the loss given default (LGD) are correlated and driven by macroeconomic variables. Paradoxically, there has been very little effort from the theoretical literature to develop credit risk models that would include this possibility. The goals of this paper are, first, to develop the theoretical, reduced-form framework needed to handle stochastic correlation of recovery and intensity, proposing a new class of models; second, to understand under what conditions would our class of models reflect empirically observed features; and, finally, to use a concrete model from our class to study the impact of this correlation on credit risk term structures. We show that, in our class of models, it is possible to model directly empirically observed features. For instance, we can define default intensity and losses given default to be higher during economic depression periods – the well-known credit risk business cycle effect. Using the concrete model, we show that in reduced-form models different assumptions (concerning default intensities, distribution of losses given default and specifically their correlation) have a significant impact on the shape of credit spread term structures and consequently on pricing of credit products as well as credit risk assessment in general. Finally, we propose a way to calibrate this class of models to market data and illustrate the technique using our concrete example using US market data on corporate yields.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Risk.net? View our subscription options

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here