ALTERNATIVE BETA RISK ESTIMATORS IN EMERGING MARKETS: THE CASE OF TUNISIA

Abstract

In this paper, we use the sample selectivity model to estimate the systematic risk for Tunisian stocks. This approach is applied in the case of extreme thin trading where data are censored due to  the presence of zero returns. The approach is a two-step procedure: a selectivity component which deals with the discreteness in the observed data and a regression component which applies to the non-zero return data. In addition, this study compares the new beta estimate to the standard OLS beta and the Dimson Beta.  The results reveal that on average, the selectivity model corrects for the general downward bias in OLS betas more suitably ten the Dimson correction. Our approach is more appropriate to deal with the presence of zero return observations associated with extreme thin trading situations in emerging markets.