Abstracts

Recently, a number of structured funds have been set up as public-private partnerships with the intent of promoting investment in energy efficiency and renewable energy in emerging and developing marktets (e.g.\ Green for Growth Fund, Global Climate Partnership Fund). The funds seek to attract institutional investors by tranching the asset pool and issuing mezzanine and senior notes. Financing of renewable energy (RE) projects is achieved via two channels: small RE projects are financed through local banks in emerging markets, whereas larger projects may be directly financed by the fund. From an investor's point of view who seeks exposure to RE projects this may appear sub-optimal: financing through local banks creates primarily credit exposures against those banks and not to RE projects. However, financing solely large RE projects may create too little diversification in the asset pool, allowing only for a small-sized senior tranche. To examine the diversification properties and RE exposure of the financing mix, we devise a bottom-up framework where the asset pool consists of infinitely granular loans via the banking channel and one large direct loan in the RE sector. Loans are modelled via asset price processes in a Merton framework with a banking systematic factor and a RE systematic factor, both of which drive the dependence in the asset pool. In a Gaussian copula framework we determine tranche hitting probabilities, tranche expected losses, tranche prices and sensitivities. Using this information we determine the optimal percentage weights of bank-transmitted RE loans and direct RE loans by maximising the sensitivity to the RE sector given a target size of the senior tranche. Our findings indicate that indeed a mix of both financing channels is optimal. Our results are robust when relaxing the simplification of assuming only one direct RE loan (as opposed to several direct RE loans) and when relaxing the simplified cash-flow structure in the asset pool, since in practice the loans will be annuities.