I understand that the Pearson correlation indicates the strength of linear relationship between two data sets. The applicability of this to hedging strategies is intuitive: If I can establish a linear relationship between two variables, I can determine how many units of X I need to buy / sell to offset my exposure to Y. My question is about the usefulness of Spearman's rho and Kendall's tau.
If I'm not mistaken, Pearson's r assumes normally-distributed data, which we know don't represent financial returns. True, we can use log the data to have fatter tails, but my fits and tests revealed the log-normal, too, is a poor approximation. So, to escape the confines of normality, I turn to rho and tau -- in which case my question is: how useful are these metrics to quantifying a hedging strategy? How would I use them? Could someone please provide an example?