This paper proposes a novel methodology to generate static/off-line signatures of new identities. The signature of the new synthetic identity is obtained particularizing the random variables of a statistical distribution of global signature properties. The results mimic real signature shapes and writing style properties, which are estimated from static signature databases. New instances, as well as forgeries, from the synthetic identities are obtained introducing a natural variability from the synthetic individual properties. As additional novelty, an ink deposition model based on a ballpoint is developed for realistic static signature image generation. The range of the static signature generator has been established matching the performance obtained with the synthetic databases and those obtained with two public databases.