Temperature modelling and weather derivatives pricing with application in Scottish electricity industry

Weather and more precisely average temperature is undoubtedly the most significant natural factor to which the electricity industry is vulnerable. Since late 1990s weather derivatives have been increasingly employed to hedge volumetric risk due to adverse temperature. This dissertation attempts to discuss weather derivatives hedging in relation to the Scottish electricity market. To achieve this goal, we first deal with the most important issue, temperature modelling, in which we consider AR(1) and ARMA models for empirical temperature simulations. In the second part, we use statistical regressions to investigate the relationship between monthly temperature and electricity consumption in Scotland. The dependency and seasonality of electricity consumption is strongly evidenced. After the relation between electricity consumption and temperature is analysed, appropriate hedging strategies are illustrated using pricing techniques such as burn analysis and Monte Carlo simulation to determine fair prices of the temperature options. Although the hedging part is rather simple due to availability of data, the implications of our findings shed some light on hedging using temperature-based derivative contracts in the increasingly de-regulated Scottish electricity industry