Quantitative tools for energy risk management

在能源生产的环境成本日益得到承认和核算的世界里，鼓励对清洁和高效生产技术的投资变得越来越重要。与能源相关的市场--电力市场、排放市场、天然气市场和其他市场--的作用至关重要。我的研究计划关注的是数学和计算模型的开发--具有良好的经济基础--寻求为研究这些市场提供工具。这些工具可用来帮助理解和管理因未来市场行为所固有的不确定性而产生的风险。许多能源和大宗商品市场相对较新，在根本上与更成熟的金融市场不同，主要是因为它们关注的是真实实物的生产和消费。模型必须考虑到许多相互关联的不确定性来源，这可能会导致高维。开发能够处理这种高维度的模型是这项提议的关键主题。决定使用什么是好的或不好的模型涉及到了解模型的用途、模型与可用数据的匹配程度以及从模型中提取有用的定量信息的容易程度。与最后一个问题相关的是我研究的另一个方面：开发用于处理能源市场模型的高效和可靠的计算算法，包括蒙特卡洛方法、有限差分方法、傅立叶方法和自适应小波方法。最后，能够评估一个人应该对这一过程中出现的数字给予多大的权重，取决于评估模型风险的工具，这是提案的第三个组成部分。

In a world where the environmental costs of energy production are increasingly being recognised and accounted for, it is becoming more and more important to encourage investment in clean and efficient production technologies. The role of energy-related markets - power markets, emissions markets, gas markets and others - is key. My research programme is concerned with the development of mathematical and computational models - with a sound economic basis - that seek to provide tools for studying these markets. Such tools can be used to help understand and manage the risks arising from the uncertainty inherent in future market behaviour.There are significant modelling and computational challenges to be faced. Many energy and commodity markets are relatively new and differ in fundamental ways from more established financial markets, mostly because they are concerned with the production and consumption of real physical things. Models have to take into account many interrelated sources of uncertainty, and this can lead to high dimensionality. Developing models that can deal with this high dimensionality is the key theme of this proposal. Deciding what is a good or a bad model to use involves understanding the use to which the model will be put, how well-suited the model is to the available data, and how easy it will be to extract useful quantitative information from the model. Related to this last question is another aspect of my research: the development of efficient and reliable computational algorithms for working with energy market models, including Monte Carlo, finite difference, Fourier and adaptive wavelet methods. Finally, being able to assess how much weight one should put on the numbers that emerge from this process relies on tools for assessing model risk, a third component of the proposal.