Prediction of large price changes in the energy market using extreme value statistics
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In this project we have first and foremost been comparing the performance of the ACER method with the POT method in the prediction of extreme values from the heavy tailed distributions; especially for data from the energy markets. The energy market is an exciting dynamic market where small singularities can make large differences in the price. Therefore it is very important and challenging to analyse and make predictions in this market. We have also analysed a dataset which is not from the energy market, to compare and see the main differences between the two markets. We have also taken in consideration of removing the return value for the dates of maturity to see whether this will have any influence on the results.The main concept of the POT method is to find a threshold, $u$, and let the excesses be distributed by the Generalised Pareto Distribution. Whilst for the ACER method, we assume a specific shape of the tail, which in this project was of the kind Fréchet. We have done this analysis for five different data sets where two of them have been considered with and without their expiration dates. We have also filtrated the data sets with an AR-GARCH filter, and then used the POT and ACER on the residuals from the process. We have found out that both methods are not greatly influenced by the filtration, but we see the tendency of the POT method predicting a heavier tail than the ACER method. Further on, we can say that there are no significant large effects of removing the return values for the dates of maturity. Lastly, the data sets from the energy market prove themselves much more heavy tailed than for the data set from Norsk Hydro.