Spillover Volatility Between Fuel Mix and Electricity Prices

Spillover Volatility Between Fuel Mix and Electricity Prices

Konstantinos Kakouris (University of Piraeus, Greece) and Dimitrios Psychoyios (University of Piraeus, Greece)
Copyright: © 2020 |Pages: 32
DOI: 10.4018/978-1-7998-2436-7.ch008

Abstract

Regulators were the only institution who set the electricity prices, including costs of transmission, distribution, and generation. Nowadays, this has changed. Electricity prices are determined by the fundamental economic rule of supply and demand. The forthcoming work examines a potential relationship between electricity price and fuel mix. The authors use the Nordic System's electricity prices and generation. They conclude that hydropower and nuclear power plays a vital role in the futures energy mix and in the stability of electricity prices. A spillover effect is detected between electricity prices and fuel mix, but a need for further research is recommended.
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Introduction

The last decades, energy markets have been liberalized starting from the fuel markets and continued with electricity markets in mid – 1990s. The distinction between energy markets is a necessity in order to facilitate research and decision making. The energy markets are categorized in three market groups: the fuel market (oil, gas, coal etc.), the electricity market and the market of emissions and insurances for sudden outages. Nowadays, electricity is exchanged in competitive markets, like other commodities, abandoning the regulated market structure. Nevertheless, electricity could be described as an unusual commodity, in a sense that, it is a non-storable product and the demand must be covered immediately. Hence, electricity is characterized as a “flow commodity” with limited storage and transportation.

Concerning the calculation of electricity prices, there are some other features, like temperature and weather conditions, season and day duration, unpredictable outages and grid congestion which accelerates the randomness of the process. The scientific community believes that, not only these features are responsible for the phenomenon of high volatility in electricity prices but also there is another opinion that fossil fuels can affect electricity prices through CO2 emissions, as well. It is based in the environmental policies of low – carbon technologies, which introduce further uncertainty in energy markets. These uncertainties in correlation with some random movements of carbon credits and fossil fuels’ prices are factors that increase the already high volatility in electricity prices. To analyze, the cost of generating electricity is sensitive to fossil fuels’ prices and fuels’ costs. To be more precise, coal plants affect electricity generation more than 35%. In other words, fossil fuels’ market prices affect electricity prices in a sense that the unpredictable prices in fossil fuels and their variability make electricity prices highly volatile (Mari, 2014). As a ramification, the forecast of the price are quite difficult and uncertain; the researchers don’t know in which extend volatility is spanned. Nonetheless, a solution was proposed in order to hedge and control the high volatility. Nuclear power is a carbon – free technology, thus, volatility becomes more sustainable and more predictable because it offers a possibility of risk hedging (Mari, 2014).

There are seven groups of electricity prices; price of high demand, price of low demand, price of different periods (seasons), price of weekends, price of working days, price of vacations and price of business activities. Taking into account the natural features of electricity and the aforementioned categories, the reader understands that, in order to calculate the precise future price of electricity is more than complex. The cause of the complexity is hidden behind the factors, affecting the prices’ volatility in electricity, which is difficult to observe and determine. The unobserved components can be named as “Unspanned Stochastic Volatility” components. These components have no information about assessments in future changes; that plays a vital role in decision making of investors, and electricity generators, as well as it provides explanations about volatility risk and hedging. The index, R2 (R – squared), can be consider as a measure of unexplained volatility or as an estimation of the amount of explained volatility. For instance, a very low price of R – squared means the existence of highly unexplained volatility (Collin – Dufresne & Goldstein, 2002).

Into this framework, electricity needs must meet four elements: cover, safety, efficiency and reasonable prices. Taking, all the above, into consideration, researchers decide to make models and develop methodologies in order to deal with these problems. Certainly, these models’ analysis demands parameters such as time period, price variances and trends. The analysis of the data and the results occur through the development of GARCH, ARCH, ARIMA, Demand Elasticity, Levelized Cost of Electricity (LCOE) and Weighted Average Cost of Capital (WACC) models. In general, the dominate opinion in literature suggests that the electricity price must be examined seasonal (seasonality) and has the trend to return in its mean price (mean reversion) by the “Invisible hand of the market”, as Adam Smith stated in his book “The Wealth of Nations”, in 1776 (Smith, 1776).

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