GMM estimation of the number of latent factors: With application to international stock markets

We propose new generalized method of moments (GMM) estimators for the number of latent factors in linear factor models. The estimators are appropriate for data with a large (small) number of cross-sectional observations and a small (large) number of time series observations. The estimation procedure is simple and robust to the configurations of idiosyncratic errors encountered in practice. In addition, the method can be used to evaluate the validity of observable candidate factors. Monte Carlo experiments show that the proposed estimators have good finite-sample properties. Applying the estimators to international stock markets, we find that international stock returns are explained by one strong global factor. This factor is highly correlated with the Fama–French factors from the U.S. stock market. This result can be interpreted as evidence of market integration. We also find two weak factors closely related to markets in Europe and the Americas, respectively.