Abstract
This master's thesis develops a pricing method for spark spread options using a Monte Carlo method. The underlying commodities of interest, natural gas and uranium highlight the prevalence of natural gas power and nuclear power in Canada. To characterize the dynamics of electricity prices and capture specific features they have, two Levy models are proposed: a jump-diffusion model and a time-changed model. Real data are used to calibrate the models, using the daily average market prices for the last five years. We created a method to compute the price of the derivative under realistic modelling conditions using parameters found through the real data. Such models can be used to value the spark spread contracts to mitigate the risk associated the contracts.
Highlights
In this chapter we will discuss the electricity market, and the Ontario electricity market
Computing prices through the model created is more complicated since the Future price is used in the payoff calculation, as opposed to the spot price
The novelty of this work lies in establishing spark prices together with the use of non-standard models
Summary
In this chapter we will discuss the electricity market, and the Ontario electricity market. To introduce the electricity market, general terminology will be explained as well as key components to general commodity markets. This project models an electricity market, with an application to Ontario market. A soft commodity market is as simple as trading wheat for something with value. This is an example of a storable commodity. There are two subclasses within Levy processes where calculations are fairly simple which are time-changed processes and multivariate jump diffusion models.
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