Alternative Beta Risk Estimators in Emerging Markets: The Latin American Case

In this paper, a new alternative beta risk estimator method designed to offer better results when coping with the situation of extreme thin trading is presented for Latin American stocks. The method proposed is applied to a set of data in which the estimator is adjusted for censoring, that is, the presence of zero returns, induced by thin trading. The procedure used is the sample selectivity model, which includes a two-step method: a selectivity equation and a regression component applied to the non-censored data. Furthermore, the study compares the resultant selectivity corrected beta to the standard OLS beta and the Dimson Beta. We illustrate the empirical behaviour of the selectivity beta estimator using a sample of stocks in six countries that are part of the emerging markets in Latin America. The results indicate that the selectivity-corrected beta does correct the downward bias of the OLS estimates and possesses desirable statistical properti