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We apply Bayesian Model Averaging and a frequentistic model space analysis to assess the pricing-determinants of credit default swaps (CDS). Our study focuses on the complete model space of plausible models covering most of the variables and specifications used elsewhere in the literature, including different copula models. The approach followed supports ultimate transparency and robustness for the empirical study at hand. Using a large data-set of CDS contracts we find that CDS price dynamics can be mainly explained by factors describing firms' sensitivity to extreme market movements. More precisely, our results suggest that dynamic copula based measures of tail dependence incorporate almost all essential pricing information making other potential determinants such as Merton-type factors or variables measuring the systematic market evolution - based on simple means or principal component analysis - negligible.

Macro-stress testing studies often rely on rather short sample periods due to the limited availability of banking data. They may fail to appropriately account for the cyclicality in the interaction between the banking system and macroeconomic developments. In this paper we use a newly constructed data set on German banks' income and loss statements over the past 36 years to model the interaction between the banking sector and the macroeconomy. Our identified-VAR analysis indicates that the level of stress in the banking sector is strongly affected by monetary policy shocks. The results rationalize the active behavior of central banks observed during periods of financial market crises.