A methodology to optimally design a financial issue to hedge non-tradable risk on financial markets is developed. It is assumed that economic agents assess their risk using monetary risk measure. The inf-convolution of convex risk measures is the key transformation in solving the related optimization problem. It is proved that when agents’ risk measures only differ from a risk aversion coefficient, the optimal risk transfer is equal to a proportion of the initial risk. It is shown that for dynamic risk measures defined through their local specifications using backward stochastic differential equations, their inf-convolution is equivalent to that of their associated drivers. In this case, the optimal risk transfer can also be characterized.
For the entire collection see [Zbl 1048.91001].