A credit derivative pricing model in a Gaussian copula framework with stochastic recovery

The recent financial crisis and a renewed focus on the modeling of systematic risk have presented challenges in the pricing and risk management of credit derivatives.  Motivated by this, researchers have pursued alternatives modeling approaches in the Gaussian copula framework, and recently models have been proposed which offer the hope of consistency by incorporating a spot, as opposed to a period, recovery rate assumption.  We extend the literature by allowing for an arbitrary distribution of spot recovery that depends randomly on systematic factors governing both default and recovery risk, introducing additional parameters that control the dependence amongst defaults, recovery rates as well as the latent factors driving these.  We illustrate empirically that our model can result in superior pricing and hedging of CDO tranches as well as how parameters of the model may be calibrated to historical loss data.