Abstract
Building on the Shanken (1992) estimator, we develop a new methodology for estimating and testing beta-pricing models when a large number of assets N is available but the number of time-series observations is small. We show empirically that our large N framework can change substantially common empirical findings regarding estimated risk premia and validity of beta-pricing models. We generalize our theoretical results to the more realistic case of unbalanced panels. The practical relevance of our findings is confirmed via Monte Carlo simulations.
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