Poor farmers find it difficult to cope with price-weather shocks through self-insurance, because they cannot afford to keep large stocks and to protect their crops through irrigation and other measures. Mutual insurance is not an option either, because all participants would be faced with the same price-weather conditions at the same time. The next option of market insurance is plagued by excessive monitoring cost in avoidance of moral hazard and adverse selection. Consequently, new types of insurance are needed. Among the arrangements suggested, index-based insurance is currently receiving much attention. Index-based insurance offers an indemnification according to an index function that depends on agreed upon price and weather conditions rather than on an assessment of damage at individual farm level. Existing proposals and experiments present a synthetic index function whose effectiveness is established by assessing its capacity to stabilize revenues on the historical record. The present paper proposes an approach that is different in that it enables the insurer to offer an indemnification that is optimal from the perspective of the farmer in preventing a fall below a specified poverty line and is self-financing up to a given subsidy. To this effect, we develop and apply a model that minimizes farmers’ risk of receiving an inadequate indemnification. The approach builds on methods from catastrophic risk management in insurance and support vector regression in statistics. It is applied to Ghana, where according to our database compiled for the period 1980-2005 on average 44 percent of the farm population fell below the poverty line. Simulations of index-based insurance show that, while a parametric form can fit the ideal indemnification fairly well (reducing poverty by about 7.5 per cent points), semi-parametric forms can perform better reducing poverty by another 15-25 per cent points, depending on the regularization.