With regards to a cross-sectional asset pricing (stocks) study, I am testing if one variable can explain another. One common approach to do this, is to use the double-sorting portfolio technique (sort on variable a into portfolios, then dependent on a - sort b into portfolio). This approach seems to be adequate for such a problem, if you have a large sample so that you get reasonable amount of dispersion in your sort variables.
Another approach I have seen in the literature is to create factors of your preferred explanatory variables by using the Fama French factor methodology (HML,SML etc), then sort you portfolios on the first variable (the variable you would like to explain) and run time series regression of a Fama French three factor model (or CAPM) augmented with your new factor.
I am wonder if there are any arguments for performing the second over the first approach? Could these approaches complement each other,
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