Who is More Averse to Random Processes?

It is well-known that various criteria for comparing aversion to real-outcome risks are equivalent. Some of this theory has been extended to Euclidean-outcome risks. We extend it further by:(a) filling the conceptual gaps, most notably by providing a criterion using our generalized Arrow-Pratt (GAP) coefficient and, (b) admitting vector outcome spaces that can be used to model risks embodied in a large class of random processes. These extensions substantially expand the tool-kit and scope of the theory. We use it to predict the effect of risk aversion on:(a) money-metric valuations of financial assets and,(b) an asset portfolio when assets are specified by dividend processes. We also provide a theoretically well-founded and computationally tractable method for estimating the GAP coefficient from data