科研之友
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摘要
Even though portfolio theory has increasingly been applied to analyze large-scale investments under uncertainty-and especially so in the electricity sector-most analysis so far has been based on the static mean-variance approach. Such an approach has two shortcomings: on the one hand, it fails to take into account irreversibility in the form of high sunk costs and the associated implications for optimal dynamic behavior. On the other hand, variance is not always the ideal risk measure, given that return or cost distributions are not necessarily normal. In fact, if large, potential losses are involved, it makes more sense to adopt a risk measure that can also take into account fat tails. In this paper, we generate these distributions arising from the investment behavior optimized in a real options model, thus accounting for uncertainty and irreversibility at the plant level, and use them in a dynamic portfolio model, where the conditional value-at-risk (CVaR) is the risk measure. More specifically, we look at the dynamics of the (CVaR-) optimal technology mix over a future time period conditional on the initial distribution of technologies, such that given energy demand is met. The application to investment in the electricity sector with uncertain climate change policy shows that this approach is not only useful from the aggregate investment point-of-view but also for the purpose of evaluating the effects of policy on investment patterns and the resulting energy mix.
- 出版日期2011-9
- 单位国际应用系统分析学会(IIASA)
