Assessing the idiosyncratic risk and stock returns relation in heteroskedasticity corrected predictive models using quantile regression

- Addressing superiority of GARCH measure of idiosyncratic volatility risk in producing positive risk-return relationship.
- Exploring idiosyncratic risk-return relationship dynamics in the tails of the returns distribution via quantile regression.
- Explaining the 'idiosyncratic risk-return puzzle' using alternative rolling regression estimation plans.
- Showing empirically that the marginal effect of idiosyncratic risk on returns is parabolic and quantile dependent.

This paper examines the superiority-claim of the GARCH based measure in resolving the 'idiosyncratic risk-return puzzle' using Australian data. The least squares and the quantile regressions of stock-returns on lagged idiosyncratic-volatility estimated from daily data using two measures (including GARCH) fail to support such claim. The quantile regression estimation reveals the risk-return relationship to be quantile dependent; it is parabolic but significant only at the extreme quantiles. The parabolic-form is convex (concave) at the lower (upper) quantiles of the returns' conditional distribution. This changing relationship-form reflects uncertainty in predicting returns. Moreover, the idiosyncratic risk-return puzzle is a model specification problem.