  In this paper, we propose a novel asymmetric $\epsilon$-insensitive pinball
loss function for quantile estimation. There exists some pinball loss functions
which attempt to incorporate the $\epsilon$-insensitive zone approach in it
but, they fail to extend the $\epsilon$-insensitive approach for quantile
estimation in true sense. The proposed asymmetric $\epsilon$-insensitive
pinball loss function can make an asymmetric $\epsilon$- insensitive zone of
fixed width around the data and divide it using $\tau$ value for the estimation
of the $\tau$th quantile. The use of the proposed asymmetric
$\epsilon$-insensitive pinball loss function in Support Vector Quantile
Regression (SVQR) model improves its prediction ability significantly. It also
brings the sparsity back in SVQR model. Further, the numerical results obtained
by several experiments carried on artificial and real world datasets
empirically show the efficacy of the proposed `$\epsilon$-Support Vector
Quantile Regression' ($\epsilon$-SVQR) model over other existing SVQR models.
