Pricing of energy by means of stochastic model

In this dissertation we present a mean-reverting jump diffusion model for pricing of energy particularly electricity by means of stochastic. We discuss the stochastic model which is used to model the behavior of electricity prices. Despite some distributional similarities with asset prices, electricity prices have dramatically different stochastic properties from those financial products even other commodities due to its non-storability nature. These properties include mean-reversion, stochastic volatility, seasonality as well as short lived spikes or jumps. The recent deregulation of electricity markets in the world has exposed power producers and users to market risk due to those unique features of energy price dynamics. The prices contain strong mean reversion, which reflects the demand and supply movements. The model developed is calibrated using the market data from Nord pool for the period from January 1997 to April 2000. The daily price model is estimated via maximum Likelihood-Conditional Characteristic Function (ML-CCF) to obtain the solution in closed form. From the model we derive the corresponding forward prices under Q-martingale measure and calculate forward prices at different expiries. All forward prices are subject to the market price of risk due to the fact that power markets are incomplete markets. The ability to model the spot prices and obtain forward price dynamics is essential when assessing the performance of heading strategies that use forward contacts.