The very specific features of the spot prices, especially occurrence of severe jumps, create a spot price risk for retailers who purchase electricity at unregulated highly volatile prices but resell it to consumers at fixed price. Therefore, it is of high im- portance to forecast whether jump is likely to occur during the next hour. However, to the best of our knowledge, such research has not been devoted to the Czech power market yet. Therefore, the aim of this thesis is to forecast the jump occurrence in the Czech day-ahead market. For this purpose we suggest four logit model spec- ifications, each containing various independent variables (for example, electricity demand, outside temperature, lagged price and various dummy variables) where the variable selection is supported by the previous literature and by the characteristic features of the spot prices. Within the in-sample period we compare the suggested models based on the values of pseudo-R squared and Bayesian information criterion. When evaluating the out-of sample performance of suggested models we apply jump prediction accuracy and confidence, but opposed to the previous literature we sug- gest a kind of sensitivity analysis which, to the best of our knowledge, has not be proposed by any other power research. JEL Classification C25, C32, C51,...

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.

E’ stato estesamente appurato che i mercati dell'elettricità mostrano mean-reversion e elevata volatilità dei prezzi. Questo lavoro utilizza un modello VAR-MGARCH al fine di cogliere queste caratteristiche presenti sul mercato dell'energia elettrica italiana (IPEX) e analizzare le interrelazioni esistenti tra le diverse regioni in cui il mercato è suddiviso. L’analisi è condotta sui prezzi giornalieri dal 1 ° gennaio 2006 al 31 dicembre 2008. I coefficienti stimati dalle equazioni condizionali indicano che i mercati regionali sono abbastanza integrati e i prezzi regionali dell'energia elettrica possono essere adeguatamente previsti impiegando i prezzi passati di ciascun mercato zonale. La volatilità e la cross-volatility sono significative per tutti i mercati, indicando la presenza di forti componenti ARCH e GARCH e la sostanziale inefficienza dei mercati. E’ inoltre evidente un’elevata persistenza della volatilità e della cross-volatility in tutti i mercati. I risultati indicano inoltre che gli shock rilevati, sia nella volatilità, sia nei vari mercati, persistono nel tempo e che in ogni mercato la persistenza è più marcata quando è causata da innovazioni stimate sulle stesso mercato rispetto a shock stimati su altre aree. Questa persistenza descrive la tendenza delle variazioni dei prezzi a raggrupparsi nel tempo. / It is commonly known that spot electricity markets show mean-reversion and high price volatility. This work employs a VAR-MGARCH model to capture these features in the Italian electricity market (IPEX) and analyze the interrelation existing among the different regions in which the market is divided. Daily spot prices from 1 January 2006 to 31 December 2008 are employed. The estimated coefficients from the conditional mean equations indicate that the regional markets are quite integrated and regional electricity prices could be usefully forecasted using lagged prices from either the same market or from the other areal markets. Volatility and cross-volatility spill-overs are significant for all markets, indicating the presence of strong ARCH and GARCH effects and market inefficiency. Strong persistence of volatility and cross-volatility are also evident in all local markets. The results also indicate that volatility innovations or shocks in all markets persist over time and that in every market this persistence is more marked for own-innovations or shocks than cross-innovations or shocks. This persistence captures the propensity of price changes of similar magnitude to cluster in time.