This talk is about joint work with Pawel Szerszen. In recent work, we developed a reduced-form model of credit risk that incorporates stochastic volatility in default intensity via stochastic time-change. Here we extend our model to a multi-name setting with common factors in both the business-time intensities and the stochastic time-change. Our particle Markov chain Monte Carlo (pMCMC) estimation method overcomes nonlinearity in the measurement equation and state-dependent volatility in the state equation. We apply the estimator to panel data of credit default swap spreads on five major banks, and find strong evidence of dependence on a common volatility factor, as well as a common factor in intensities. Implications for forecasting the probability of systemic events in the finance sector are illustrated.