Modern Portfolio Theory is a theory which was introduced by Markowitz, and which suggests the building of a portfolio with assets that have low or, in the best case, negative correlation. In times of financial crises, however, the positive diversification effect of a portfolio can fail when Traditional Assets are highly correlated. Therefore, many investors search for Alternative Asset classes, such as Renewable Energies, that tend to perform independently from capital market performance. 'Windfall Profit in Portfolio Diversification?' discusses the potential role of Renewable Energy investments in an institutional investor’s portfolio by applying the main concepts from Modern Portfolio Theory. Thereby, the empirical analysis uses a unique data set from one of the largest institutional investors in the field of Renewable Energies, including several wind and solar parks. The study received the Science Award 2012 of the German Alternative Investments Association ('Bundesverband Alternative Investments e.V.').

Textprobe: Chapter 4.3, Discussion of the asset-only perspective: The pioneering work by Dunlop (2004a) revealed that when holding a portfolio of wind parks, they more and more resemble bond rather than equity investments. This is mainly due to smoother returns that can be expected. Therefore, a straightforward approach would be to find the optimal weights according to the MVP and TP by solving the optimization problem in equation [1] and [2]. Unfortunately, there are some areas of conflict that have to be considered when applying the mean-variance framework to Renewables. While the following part of the present paper will focus the attention on wind investments, the ideas can be applied to solar parks accordingly. The first area of conflict relates to the fact that within stocks, one can see the entire investment universe at once whereas with wind parks one cannot. This means that one can e.g. use all stocks from an index and find the respective weights for the optimal portfolios. Similarly, one could choose a representative index for several asset classes (e.g. bonds, stocks and Alternatives) and optimize accordingly. For wind farms, however, this approach might not be appropriate since each wind farm is unique in the sense that it can be put into the portfolio only once. Let alone short-selling is not possible with a wind farm. Therefore, finding the optimal weight for a respective wind farm does certainly not guarantee that the investor can also implement this recommendation, as he might not be able to perfectly replicate the farm. A second shortcoming concerns the illiquidity of the asset class. According to Pforte et al. (2008), the wind farm market is a private market without a public trading place and in general low transparency. Wind farms come to the market once and are usually not traded within their operational lifetime. When the farms are traded, transaction costs are usually a lot higher than for stocks and bonds, since during the acquisition phase intensive technical and legal due diligence have to be undertaken. Due to these circumstances, there might be an additional risk for this asset class, which cannot easily be incorporated into the mean-variance framework. Since the investor is committed to a long-term asset, which he cannot easily sell off, the resulting weights in the optimal portfolio for these investments might be overstated. A final shortcoming relates to the usual trade-off between risk and return. If investors are risk-averse, then they are generally willing to invest in a riskier asset only when it also delivers a higher expected return. In a capital market equilibrium model such as the CAPM, this positive linear relationship is reflected in the Security Market Line (SML). The SML is simply the graphical representation of equation [3] in the beta-return space. In equilibrium, each asset must be priced so that it exactly falls on the SML. In his work on wind farms, Dunlop (2004a) states that the trade-off between risk and return might not exist for this asset class. This is due to natural competitive advantages of certain countries with strong and steady wind resources vis-à-vis others. Dunlop therefore suspects that wind parks would not neatly line up on a SML. This, however, is only approximately shown in his study, with capacity utilization as a proxy for returns and production volatility as the measure for risk. While the argument by Dunlop that wind farms would not line up on the SML appears intuitive, it totally abstracts from the underlying regulatory structures. It could be that the market indeed prices” the additional benefit from having e.g. a stable fixed feed-in tariff. Subsequently, the required returns for a variable price regime would have to be higher due to higher risk. However, in this case Dunlop expects the private equity market for wind to be highly inefficient across borders. Although several shortcomings have already been discussed, there is particularly one area where MPT might be even more appropriate compared to other asset classes: the single-index model. In his seminal article, Dunlop (2004a) uses a CAPM-style single-index model in order to assess diversification benefits within a portfolio of wind investments. A critical variable thereby is the right choice of the market portfolio. Since there currently does not exist a public benchmark index for wind investments, Dunlop simply creates one from all the wind parks in his data set. Subsequently, he regresses each single wind farm against the index in order to receive the respective betas for the individual wind parks. The empirical findings of Dunlop suggest that a CAPM-style single-index model has several advantages over the mean-variance framework. First, the betas from the single-index model can give a rough guide to what wind regions might be profitable instead of focusing on one specific wind farm that had to be replicated for the optimal portfolios. Second and foremost, unlike stocks, betas for wind and solar parks should be stable over time. This stems from the fact that both wind and irradiation vary around a certain long-term mean as previously shown in Figures 4 and 5. Both the variation and the mean, however, are known in advance, which implies that past betas can accurately predict future ones. For stocks, on the contrary, high betas in the past do not necessarily turn into high betas in the future. Third and last, the advantage of predictability should also apply to the correlation structures of wind and solar investments. There is no logical reason why the correlation matrix should substantially change over time. For most other asset classes, however, correlations do vary significantly in the course of time. For instance, correlations between stocks are substantially higher during bear markets. Since the notion of a global crisis does not apply to Renewables (at least regarding the main input resource), a CAPM-style model might lead to an accurate estimate of the 'unchanging' correlation structure.

• Portfolio Theory

• Alternative Assets

• Infrastructure

• Wind Energy

• Solar Energy

• Investment Analysis

• Economics

• financial crise

Frederik Bruns, M.Sc, was born in Stuttgart, Germany in 1987. He has a Bachelor's degree in Political Economics from the University of Heidelberg, and he started a Master’s program in Economics at the University of Bonn where he has a major in finance. The author was part in an international exchange program with New York University. There, his knowledge in financial markets and portfolio theory was further developed for he attended courses at the Finance Department of NYU Stern School of Business. During his studies, the author completed several internships in multinational companies, research institutions and political organizations in Germany, the U.S. and Hong Kong. Now, he is working as a Financial Analyst in the Renewable Energy division of Allianz Capital Partners in London.