  A factor copula model is proposed in which factors are either simulable or
estimable from exogenous information. Point estimation and inference are based
on a simulated methods of moments (SMM) approach with non-overlapping
simulation draws. Consistency and limiting normality of the estimator is
established and the validity of bootstrap standard errors is shown. Doing so,
previous results from the literature are verified under low-level conditions
imposed on the individual components of the factor structure. Monte Carlo
evidence confirms the accuracy of the asymptotic theory in finite samples and
an empirical application illustrates the usefulness of the model to explain the
cross-sectional dependence between stock returns.
