There isn't a standard range, if in doubt, generate a contour plot of the cross-validation criterion as a function of the hyper-parameters and see if the minimum is near one of the edges of the plot, and if this is the case, extend the grid-search in that direction.
Note that if the problem is (near) linear, IIRC the "optimal" kernel parameter value tends to minus infinity in an attempt to make the smoothest kernel possible. So if that happens you may want to try a linear SVM as well.
As @danjeharry (+1) suggests, the grid-search you are using at the moment is a bit coarse and you may get better results using a finer resolution grid search in the vicinity of the coarse grid search you have already performed. I would advise against making the step size less than say 1/2 as this can lead to over-fitting the model selection criterion and performance getting worse rather than better.