Abstracts
Tuesday June 3, 14:00-14:30 | session 2.6 | Energy Finance | room L
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.
Tuesday June 3, 14:30-15:00 | session 2.6 | Energy Finance | room L
Operators of photovoltaic (PV) and wind power systems in Europe are faced with considerable economic challenges. Specifically, governments scale back protective measures and subsidy levels, meaning that revenues must increasingly be realized in competitive markets under two constraints. Firstly, forecast errors trigger costly intraday efforts to restore the balance between day-ahead sales and actual production. Secondly, strong output correlations between geographically proximate wind or PV assets lead to below-average prices in times of strong winds or solar irradiation.
This paper examines the extent to which portfolio effects and intraday trade optimization can improve the market value of PV and wind assets. To this end, a two-stage model is formulated. In the first stage, forecasted supply and demand volumes are committed day-ahead, and economic value is being added thanks to portfolio effects between expected PV supply, wind supply, and power demand.
In the second stage, forecast errors in the portfolio's day-ahead power supply are traded jointly with flexible loads in the intraday market, accounting for two complexities. On the one hand, forecast errors remain inaccurate throughout the trading period; frequent forecast updates reduce, but do not eliminate uncertainty. On the other hand, limited liquidity and frequent changes in the trading activity of market participants cause large intraday price variations over time and across delivery slots. In order to maximize value under such uncertain and dynamic conditions, the model is configured such that it improves both the timing and the volumes of trades.
The timing of trades is determined by means of an options analysis. The intraday stage is split into several trade windows, and the optimal window for trading is chosen by iteratively comparing the value of trading immediately versus trading after waiting for reduced uncertainty in the next window. This method is found to increase efficiency, since changing volatility and risks are accounted for in the trading decisions. The value-added depends strongly on the underlying volatility.
Trade volumes are optimized through demand response, created when flexible loads are shifted between delivery slots based on a price ranking. This measure increases (decreases) the portfolio's supply-demand balance for delivery slots with short-term price jumps (drops). The intraday value of demand response is found to exceed the economic value of committing flexible loads day-ahead.
Tuesday June 3, 15:00-15:30 | session 2.6 | Energy Finance | room L
Risk premia between spot and forward prices play a key role in energy markets. This paper derives analytic expressions for such risk premia when spot prices are modelled by Levy semistationary (LSS) processes. We find that there is a structural difference between geometric and arithmetic models based on LSS processes. While there is always a stochastic component in the risk premium in a geometric model this is not necessarily true in an arithmetic model. Moreover, we show that when working with a structure-preserving change of measure between the physical and the risk-neutral probability measure, the stochastic volatility is a key component, in particular in the arithmetic model, where it can solely introduce stochastic behaviour in the risk premium. For particular choices of the stochastic volatility process, e.g. for a square-root diffusion or a non-Gaussian Ornstein-Uhlenbeck process, we show how the dynamics of the stochastic volatility process lead to stochastic dynamics of the risk premium.
Electricity is a special case within the class of energy commodities since it is essentially non-storable. We focus on electricity in our empirical work and investigate to what extent the conditional expectation of the average spot price under the physical probability measure has any explanatory power for the corresponding electricity futures. In our empirical study, we focus on the short-end of the forward curve only and use a daily time series of the front months of Phelix Peakload Month Futures from the European Energy Exchange market from Oct 2009 – Sep 2012. Despite the fact that our LSS-based model fits the spot prices very well, we find that the corresponding conditional expectation has only some explanatory power for the futures. There is still a significant amount of variability in the futures which cannot be explained by our predicted average spot prices. Hence, either one models the risk premium directly to find a suitable model for electricity futures or one could model the futures directly. Here we follow the latter approach, where we postulate a model for electricity futures based on the theoretical results obtained for the conditional expectation of the average spot (based on an LSS process). We calibrated such a model directly to the futures and obtained a good model fit.