Virtual characters capable of showing emotional content are considered as more believable and engaging. However, in spite of the numerous psychological studies and machine learning applications trying to decode the most salient features in the expression and perception of affect, there is still no common understanding about how affect is conveyed through body motions. Based on findings reported by the psychology research community and quantitative results obtained in the computer animation domain during the last years, we propose to represent affective bodily movement through a low-dimensional parameterization consisting of the spatio-temporal trajectories of eight main joints in the human body (hands, head, feet, elbows and pelvis). Using a combined evaluation protocol, we show that this low-dimensional parameterization and the features derived from it are a compact and sufficient representation of affective motions that can be used for automatic recognition of affect and the generation of new affective-expressive motions.