Title: Estimating risk premia using large cross-sections
Authors: Valentina Raponi - Imperial College London (United Kingdom) [presenting]
Abstract: A limiting theory is presented for estimating and testing linear asset-pricing models when a large number of assets, $N$, is available, together with a fixed, possibly small, time-series dimension, $T$. Since the ordinary least squares (OLS) estimator is biased and inconsistent in this case, we focus on an alternative estimator, which we show to exhibit many desirable properties. We formally prove its consistency and derive its asymptotic distribution, showing how its limiting variance can be consistently estimated. We also propose a new test of the no-arbitrage asset pricing restriction, and establish its asymptotic distribution (assuming that the restriction holds). Finally, we show how our results can be extended to deal with the more realistic case of unbalanced panels. The practical relevance of our findings is demonstrated using Monte Carlo simulations and an empirical application to asset-pricing models with traded risk factors. Our analysis suggests that the market, size, and value factors are often priced in the cross-section of NYSE-AMEX-NASDAQ individual stock returns over short time spans.