Quantitative tools for energy risk management

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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.