We consider estimation and goodness–of–fit tests in GARCH models with innovations following a heavy–tailed and possibly asymmetric distribution. Although the method is fairly general and applies to GARCH models with arbitrary innovation distribution, we consider as special instances the stable Paretian, the variance gamma and the normal inverse Gaussian distribution. Exploiting the simple structure of the characteristic function of these distributions we propose minimum distance estimation based on the empirical characteristic function of properly standardized GARCH–residuals. The finite–sample results presented facilitate comparison with existing methods, while the new procedures are also applied to real data from the financial market.