This paper describes a novel method for the generation of synthetic handwritten signatures, in the form of a series of time-stamped pen data channels, for use in dynamic signature verification experimentation. The technique introduces modelled variability within the generated data based on variation that is naturally found within genuine source data. Experimentation using the SVC2004 dataset and a commercial signature verification engine shows that the synthesized data achieves comparative verification performance to the use of genuine data.