Dependent default times with mixed hitting-time models

Mixed hitting-time models allow the analysis of competing risks through optimal stopping decisions interpreted as crossing times of latent Lévy processes with heterogeneous thresholds. In this paper, we consider a bivariate time model with dependent default, where observation times are subject to censoring and share a common latent process given by a Lévy subordinator. We establish the identifiability of the model and propose different estimators for the marginal distributions and the joint survival function. We establish their asymptotic properties and evaluate the finite-sample performance of our results through a simulation study on synthetic data, followed by an application using real data.