Modelling commodity prices in the Australian National Electricity Market

Thomas, S 2007, Modelling commodity prices in the Australian National Electricity Market, Doctor of Philosophy (PhD), Economics, Finance and Marketing, RMIT University.


Document type: Thesis
Collection: Theses

Attached Files
Name Description MIMEType Size
Thomas.pdf Thesis application/pdf 2.10MB
Title Modelling commodity prices in the Australian National Electricity Market
Author(s) Thomas, S
Year 2007
Abstract Beginning in the early 1990s several countries, including Australia, have pursued programs of deregulation and restructuring of their electricity supply industries. Dissatisfaction with state-run monopoly suppliers and a desire for increased competition and choice for consumers have been the major motivations for reform. In Australia, the historical, vertically-integrated, government-owned electricity authorities were separated into separate generation, transmission, distribution and retail sectors in each State and a competitive, wholesale market for electricity, the National Electricity Market (NEM) began operation in December 1998.

The goal of deregulation was (and remains) increased competition in electricity supply, so that consumers may enjoy wider choice and lower prices. The first benefit has largely been delivered but it is arguable whether the second benefit of lower prices has been realised. Increased competition has come at the price of increased wholesale price volatility, which brings with it increased cost as market participants seek to trade profitably and manage the increase in price risk. In the NEM, generators compete to sell into a pool market and distributors purchase electricity from the pool at prices determined by demand and supply, on a half-hourly basis. These market-clearing prices can be extremely volatile. Electricity prices are generally characterised by significant seasonal patterns, on an intra-day, weekly and monthly basis, as demand and supply conditions vary. Prices are also characterised by strong mean-reversion and extremely high spikes in price. While long-run mean prices typically range between $30 and $45 per megawatt hour, prices can spike to levels above $9,000 or $10,000 per megawatt hour from time to time. These spikes tend to be sporadic and very short-lived, rarely lasting for more than an hour or two. Although infrequent, spikes are the major contributor to price volatility and their evolution and causes need to be investigated and understood.

The purpose of this thesis is to investigate and model Australian electricity prices. The research work presented is mostly empirical, with the early analytical chapters focusing on investigating the presence and significance of seasonal factors and spikes in electricity price and demand. In subsequent chapters this work is extended into analysis of the underlying volatility processes and the interaction between extreme values in demand and price is specifically investigated. The findings of the thesis are that while the characteristics of strong seasonal patterns and spikes that are generally observed in similar electricity markets are present in the NEM in both price and demand, there is significant variation in their presence and effect between the regional pools. The study also finds that while time-varying volatility is evident in the price series there is again some variation in the way this is characterised between states. A further finding challenges the accepted wisdom that demand peaks drive price spikes at the extremes and shows empirically that price spikes are more likely to be caused by supply disruptions than extremes of demand. The findings provide useful insight into this highly idiosyncratic but economically important national market.
Degree Doctor of Philosophy (PhD)
Institution RMIT University
School, Department or Centre Economics, Finance and Marketing
Keyword(s) Electricity
Microstructure
Seasonalities
Outliers
GARCH
Event Studies
Versions
Version Filter Type
Access Statistics: 330 Abstract Views, 1136 File Downloads  -  Detailed Statistics
Created: Mon, 29 Nov 2010, 16:09:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us