Forecasting stylised features of electricity prices in the Australian National Electricity Market

Abstract

This thesis tests whether forecast accuracy improves when models that explicitly capture the stylised features of the Australian National Electricity Market (NEM) are employed to generate predictions. It is believed that by explicitly modelling these features of electricity wholesale spot prices, the accuracy of the price forecast models can be improved when compared to standard alternative. The stylised features identified in data are mean-reversion, sudden short-lived and consecutive jumps and heavy tails. When employing models to capture the stylised features of electricity prices, the models necessarily become more complex and often contain a greater number of parameters which combine to mimic the characteristics observed in the price series. Throughout this thesis an adherence to the principle of parsimony (Makridakis, et al page 609) will be maintained; that is if two models effectively generate the same forecast performance the simpler model will be preferred whether it contains the stylised features or not. This is also known as Occum’s Razor. This investigation is important in terms of a better understanding of what models are more useful has the potential to lead to more accurate price forecasts which may result in less volatility in market prices leading to more efficient markets. Further, by assessing models that capture various stylised features it may be possible to infer the importance of particular features. Given that wholesale prices are a major determinant of how much end users pay for powering their homes and businesses, it is believed that a better understanding of what forecasting models work (and do not) will allow market participants to develop more successful (business) strategies for adjusting supply to meet demand and to assist with the valuation of financial assets as part of risk management. Additionally, a better understanding of the dynamics of electricity prices and its implications for successful forecasting is important for government policy makers, as Government sets the rules that govern the production and distribution of electricity. It is believed that by explicitly modelling the stylised features of electricity wholesale prices, forecast accuracy can be improved upon baseline models commonly used in quantitative finance. This thesis investigates the forecasting ability of two distinct modelling approaches which by construction capture the stylised characteristics of electricity prices. Namely, these are linear continuous time and non-linear modelling methods. The AR-GARCH model is chosen to be the standard approach in forecasting price series (Engle, 2001) and is taken as the benchmark model in this thesis. More specifically, this thesis aims to answer the following research questions: Does the application of continuous-time models in capturing the stylised features of Australian electricity wholesale spot prices improve forecasting ability upon the traditional AR-GARCH model? Does the application of non-linear forecast models in capturing the stylised features of Australian electricity wholesale spot prices improve forecast ability upon traditional AR-GARCH model? The continuous-time models examined in this thesis are; Geometric Brownian Motion (GBM), Mean-Reverting, and Mean-Reverting Jump-Diffusion processes. The inclusion of GBM in this thesis is due to it being the foundation for the Mean-Reverting and Jump-Diffusion models, which are considered in this thesis. Continuous-time models capture some of the main stylised features of electricity prices; Mean-Reverting process captures the mean-reversion (tendency of electricity prices to revert back to its long-term average over time) characteristics of electricity prices whilst Mean-Reverting and Jump-Diffusion process models the sudden jumps prevalent in Australian electricity prices. The models are in order such that each successive model extends the one preceding it. Note that each extension addresses a stylised feature of the data therefore the a priori expectation is that the forecasting performance will improve. The inclusion of the non-linear approach to forecasting Australian electricity prices is performed with the application of a Markov Regime-Switching model and the application of Extreme Value Theory (EVT) into electricity price modelling. The Markov Regime-Switching model is a non-linear modelling tool that is able to capture consecutive spikes prevalent in electricity prices that Mean-Reverting and Jump-Diffusion processes fail to capture. The application of EVT is included in this thesis so that heavy tails present in electricity prices can be adequately captured. Copulas are considered as a unique method that models the dependence structure of data. The forecasts based on the EVT model is built upon the application of Copula functions as these functions model the interdependence of prices within the separate regions of the Australian electricity markets. The models examined in this thesis are: 1. AR(1)-GARCH(1) 2. Geometric Brownian Motion 3. Mean-Reverting Model 4. Mean-Reverting and Jump-Diffusion Model 5. Markov Regime-Switching Model with spike distributions modelled with 6. -Gaussian distribution 7. -Log-Gaussian distribution and, 8. Extreme value Theory and Copula functions Each model under investigation mimics a known characteristic of electricity prices. Comparative performance evaluations of each model investigated in this thesis showed that the benchmark model is providing superior short-term forecasting ability