Thesis Defense :: Pricing and Risk Management in Competitive Electricity Markets
Restructuring the electric power industry has become a global trend. The traditional, vertically integrated electricity industry has been restructured into three separate industrial segments: generation, transmission, and distribution; and since the 1990s, competitive electricity markets have emerged. My thesis addresses issues on electricity spot prices modeling, electricity supply contracts pricing, and risk measure estimation in a competitive electricity industry.
Modeling electricity spot prices is essential for asset and project valuation as well as risk management. I introduce the mean-reversion feature into a classical variance gamma model to model the electricity price dynamics. The generalized method of moments and the Markov Chain Monte Carlo method are proposed to estimate parameters. Derivatives pricing formulae are derived through transform analysis.
Under a realistic electricity price model with mean-reversion and jumps, pricing and hedging customized power contracts is very challenging. A tolling agreement (or tolling contact) is one example of customized power contract in which a contract buyer reserves the right to take the output of an underlying electricity generation asset by paying a predetermined premium to the asset owner. I propose a real options approach to value a tolling contract incorporating operational characteristics of the generation asset and contractual constraints. Two simulation-based methods are proposed to solve the valuation problem. The effects of different electricity price assumptions on the valuation of tolling contracts are examined. Based on the valuation model, I also propose a heuristic scheme for hedging tolling contracts and demonstrate the validity of the hedging scheme through numerical examples.
An estimator of conditional value-at-risk (VaR) based on a GARCH(1, 1) model with heavy tailed innovations is proposed. The normal approximation method and a data tilting method are proposed to constructe confidence intervals for the conditional VaR estimator and assess their accuracies by simulation studies.