The recent period of intensified electricity price volatility has challenged both private households and businesses, resulting in companies transitioning their strategies to address the uncertainties that follow. No previous study has analysed how electricity price volatility affects Swedish industries’ returns and electricity usage. Therefore, this essay aims to fill this knowledge gap and capture the impact of electricity price volatility on two different industries, one more electricity-intensive industry and one less electricity-intensive industry. By applying a DCC-GARCH model, the study examines the impact on the returns and electricity usage of two industries to analyse if electricity price volatility has affected businesses in terms of returns and electricity consumption.  The first DCC model was run on weekly electricity spot price data from Nord Pool and data constructed through a proxy of each industry’s average return. The results show that there is almost zero conditional correlation over time, ranging from 0.03 to 0.045, between the electricity price and each industry’s returns. There are no short-run spillover effects from electricity price volatility on the on the industries’ average returns. On the other hand, there is a long term spillover effect from electricity prices to the more electricity-intensive industry and no long term spillover for the less electricity-intensive industry.  The second DCC-GARCH modell is applied on monthly electricity spot prices from Nord Pool and data of monthly electricity usage of each industry. The industries are sorted by Swedish Standard Industrial Classification (SNI). SNI 17 stands for manufacture of paper and paper products and SNI 24 stands for manufacture of basic metals. The results from this part indicates that the dynamic conditional correlation between electricity price and the electricity usage in the paper industry is close to zero which differ from the basic metal industry which is positive. Beyond the dynamic conditional correlation, we find a short-term spillover effect from electricity price volatility to electricity usage in the basic metal industry, which is absent in the paper industry. On the other hand, there is a long-term spillover effect from electricity price volatility to electricity usage in the paper industry, which is absent for the electricity usage in basic metal industry.  Overall, our study shows that the businesses in the return proxy have preformed relatively well despite an uncertain period of volatile electricity prices. Simultaneously we find that the results for the industries electricity usage differ between the chosen industries.

Electricity spot prices are difficult to predict since they depend on different unstable and erratic parameters, and also due to the fact that electricity is a commodity that cannot be stored efficiently. This results in a volatile, highly fluctuating behavior of the prices, with many peaks. Machine learning algorithms have outperformed traditional methods in various areas due to their ability to learn complex patterns. In the last decade, deep learning approaches have been introduced in electricity spot price prediction problems, often exceeding their predecessors. In this thesis, several deep learning models were built and evaluated for their ability to predict the spot prices 10-days ahead. Several conclusions were made. Firstly, it was concluded that rather simple neural network architectures can predict prices with high accuracy, except for the most extreme sudden peaks. Secondly, all the deep networks outperformed the benchmark statistical model. Lastly, the proposed LSTM and CNN provided forecasts which were statistically, significantly superior and had the lowest errors, suggesting they are the most suitable for the prediction task. / Elspotspriser är svåra att förutsäga eftersom de beror på olika instabila och oregelbundna faktorer, och också på grund av att elektricitet är en vara som inte kan lagras effektivt. Detta leder till ett volatilt, fluktuerande beteende hos priserna, med många plötsliga toppar. Maskininlärningsalgoritmer har överträffat traditionella metoder inom olika områden på grund av deras förmåga att lära sig komplexa mönster. Under det senaste decenniet har djupinlärningsmetoder introducerats till problem inom elprisprognostisering och ofta visat sig överlägsna sina föregångare. I denna avhandling konstruerades och utvärderades flera djupinlärningsmodeller på deras förmåga att förutsäga spotpriserna 10 dagar framåt. Den första slutsatsen är att relativt simpla nätverksarkitekturer kan förutsäga priser med hög noggrannhet, förutom för fallen med de mest extrema, plötsliga topparna. Vidare, så övertränade alla djupa neurala nätverken den statistiska modellen som användes som riktmärke. Slutligen, så gav de föreslagna LSTM- och CNN-modellerna prognoser som var statistiskt, signifikant överlägsna de andra och hade de lägsta felen, vilket tyder på att de är bäst lämpade för prognostiseringsuppgiften.