Laskentatoimen ja rahoituksen yksikkö, 2019
Laskentatoimi ja rahoitus
Master's Degree Programme in Finance
This Master’s thesis studies spot- and futures pricing in the Nordic electricity markets. Electricity markets provide an interesting and challenging framework for financial research. Studies of electricity derivatives pricing are usually based on the Risk Premium literature, but this thesis also discusses whether electricity futures pricing could be modeled from the perspective of the Theory of Storage.
The data set consists of daily spot electricity prices, monthly futures on spot electricity, and 13 explanatory variables. The explanatory variables include Nordic water level factors, Nordic weather temperature factors, several fuel price proxies, and market risk / sentiment variables. The sample period begins 1.1.2005 and ends 31.12.2015.
Electricity prices are highly volatile and often extreme. Extreme prices are known as price spikes in the literature. To study the tail behaviour of prices and price spikes, the thesis studies the entire spot price distribution using quantile regression methodology. The thesis continues by studying the risk premiums of electricity futures using reduced form model originally introduced to the literature by Bessembinder & Lemmon (2002) and Longstaff & Wang (2004). Finally, the thesis combines the findings of previous two hypotheses in order to develop an optimally performing model of the Nordic futures pricing.
The thesis provides contribution to the existing literature by identifying significant factors across the spot price distribution and by studying how those factors affect risk premiums in the derivative markets. The thesis also contributes to the discussion regarding the concept of Indirect Storability in electricity futures pricing. Moreover, the thesis provides contribution by developing a population weighted average temperature index for the Nordic countries. The daily index is obtained from 58 different weather observation stations throughout the Nordic countries. Temperatures are weighted by the population living in the proximity of the weather observation station to better understand how local weather conditions affect the demand for electricity.
Electricity Market, Nord Pool, Risk Premium, Quantile Regression