Cesare Robotti (Imperial College) on "Ex-post risk premia and tests of multi-beta models in large cross-sections"

Speaker:  Cesare Robotti - Imperial College
  Wednesday, May 18, 2016 at 12:30 PM Polo Santa Marta, Via Cantarane 24, Room 1.59
A limiting theory for estimating and testing linear beta-pricing models with a large number of assets and a fixed time-series sample size is presented.
Since the ordinary least squares (OLS) estimator of the ex-ante risk premia is asymptotically biased and inconsistent in this context, the focus of the
paper is on the modified OLS estimator of the ex-post risk premia proposed by Shanken (1992). We derive the asymptotic distribution of this estimator
and show how its limiting variance can be consistently estimated. In addition, we characterize the asymptotic distribution of a cross-sectional test of the
fundamental beta-pricing relation. Finally, we show how our results can be extended to deal with unbalanced panels. The practical relevance of our
findings is demonstrated using Monte Carlo simulations and an empirical application to beta-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.
Overall, there is not much evidence of pricing for the profitability and investment factors of Fama and French (2015).

Programme Director
Roberto Renò

Publication date
April 14, 2016