This paper develops a simultaneous model and moment selection procedure for factor copula models. Since the density of the factor copula is generally not known in closed form, widely used likelihood or moment based model selection criteria cannot be directly applied on factor copulas. The new approach is inspired by the methods for GMM proposed by Andrews (1999) and Andrews & Lu (2001). The consistency of the procedure is proved and Monte Carlo simulations show its good performance in finite samples in different scenarios of sample sizes and dimensions. The impact of the choice of moments in selected regions of the support on model selection and Value-at-Risk prediction are further examined by simulation and an application to a portfolio consisting of ten stocks in the DAX30 index.

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