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.

中文翻译：

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因子Copula模型中的模型和矩选择*

本文为因子copula模型开发了同时模型和矩选择程序。由于以闭合形式通常不知道因子系的密度，因此不能将广泛使用的基于可能性或基于矩的模型选择标准直接应用于因子系。这种新方法受到了Andrews（1999）和Andrews＆Lu（2001）提出的GMM方法的启发。证明了该方法的一致性，并且蒙特卡洛仿真显示了在不同样本大小和尺寸情况下有限样本中的良好性能。通过模拟以及将其应用于DAX30指数中包含十只股票的投资组合，进一步检查了支撑的选定区域中的矩量选择对模型选择和风险值预测的影响。