Site selection to test an integrated approach to agricultural research for development: combining expert knowledge and participatory Geographic Information System methods

The sub-Saharan Africa challenge programme is designed to address the problems of failures of agricultural markets, inappropriate policies and natural resource degradation, that contribute to the continuing deterioration of livelihoods and food security in the region. It is seeking to do this by redefining the roles of scientists and farmers through collaborative learning processes, addressing questions about the level, timing, type and formof participation, as well as themost effective approaches andmethods to foster them. The research domains of the programme deal with sustainable intensification of smallholder agriculture, the sustainable management of natural resources, the development of efficient markets, and the promotion of enabling policies. One question that was addressed in designing the initiative was deciding where to work so as to maximize the chances of successful testing of this new approach, so that it would lead to significant reductions of rural poverty. A participatory process was put in place to design a framework to accomplish this site selection, and then to apply it in west, east and southern Africa. A mixture of spatial data analysis and expert knowledge on spatial and non-spatial factors was used, and one primary site in each region was identified to form the basis for the next phase of the challenge programme. Several lessons were learnt from the process, including the importance of adapting themethods to actual conditions, the increased utility of targetting when the quantitative and the qualitative are freely combined, and the continued need for baseline spatial and non-spatial data to improve the targeting of research in the future that is designed to alleviate poverty in sub-Saharan Africa.     

Model-driven statistical arbitrage on LETF option markets

In this paper, we study the statistical properties of the moneyness scaling transformation, which adjusts the moneyness coordinate of the implied volatility smile in an attempt to remove the discrepancy between the IV smiles for levered and unlevered ETF options. We construct bootstrap uniform confidence bands which indicate that the implied volatility smiles are statistically different after moneyness scaling has been performed. An empirical application shows that there are trading opportunities possible on the LETF market. A statistical arbitrage type strategy based on a dynamic semiparametric factor model is presented. This strategy presents a statistical decision algorithm which generates trade recommendations based on comparison of model and observed LETF implied volatility surface. It is shown to generate positive returns with a high probability. Extensive econometric analysis of the LETF implied volatility process is performed including out-of-sample forecasting based on a semiparametric factor model and a uniform confidence bands' study. These provide new insights into the latent dynamics of the implied volatility surface. We also incorporate Heston stochastic volatility into the moneyness scaling method for better tractability of the model.