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The task of pricing options is one with many different solutions, and overtime more complicated models of the markets have been developed in anattempt to replicate assets more accurately. In this thesis we investigate theuse of neural networks for pricing within these models. As speed is of equalconcern for option pricing, the offline training period of neural networks isa desirable property for this use. Implementing neural networks is not assimple as it may seem though and as we find out, many adaptations havebeen made to try and rectify the issues this implementation introduces. Welook more closely at methods where neural networks are used for calibrationof model parameters.

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This thesis attempts to apply cluster analysis to the game of football wherea set of football players were clustered into subsets based on data gatheredover a season. Two different types of clustering algorithms were used and theresults from both these methods were then looked at discussed. A couple ofmethods of choosing the number of clusters for the input in the k-means algorithmwas also compared with each other to determine a suitable number ofclusters for the data set. Looking at the result from the clustering algorithmsand also the methods of choosing the number of clusters its obvious that theused data set it homogeneous and no distinct clusters could be found (usingthe methods in the thesis). It is still possible to draw conclusions from theresult but as cluster analysis is often used initially in data exploration there isno need for a real conclusion and the result can still be interpreted differentlybased on how the result is looked at. / <p>Hade tydigare serienummer 2022:11 men det var en dublett så bytt till 2022:63.</p>

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