Electricity markets around the world have been subject to deregulation for the last few decades, and the highly volatile characteristics of the spot prices have increased the need for practical risk management tools for actors in the market. The objective of this thesis is to examine the performance of static and dynamic hedging models with monthly and quarterly futures in the Nordic power market from 2005 to 2018. This is done by constructing hedged portfolios with a time-invariant hedge ratio from the naïve and ordinary least squares model, and time-varying hedge ratios from the constant conditional correlation GARCH model and the dynamic conditional correlation GARCH model. It is found that the best-performing GARCH model significantly outperforms the best-performing static model, both in-sample and out-of-sample. Furthermore, the results show that hedging performance varies considerably across periods. Specifically, an indication is found that the relative advantage of the GARCH models compared to the static models is greater when the volatility in the spot market is high. Measuring performance by mean-variance utility shows smaller differences in performance between the static and dynamic hedging models, but the GARCH models still rank highest overall. This suggests that dynamic hedging is beneficial also for hedgers that are utility-maximizing. Last, it is found that the hedging models obtain a significantly lower variance and value at risk when hedging with monthly contracts compared to quarterly contracts. In conclusion, the results show that futures hedging reduces the portfolio risk significantly compared to a no-hedge strategy, and this result is especially evident for the dynamic hedging models. From a hedger’s perspective, this thesis emphasizes the benefits of dynamic hedging with futures in the Nordic power market. The recommended strategy for actors hedging monthly and quarterly deliveries is to dynamically adjust the hedge ratio in their portfolios according to a constant conditional correlation GARCH model.
|Educations||MSc in Applied Economics and Finance, (Graduate Programme) Final Thesis|
|Number of pages||133|