Some contributions to the clustering of financial time series and applications to credit default swaps

Abstract : In this thesis we first review the scattered literature about clustering financial time series. We then try to give as much colors as possible on the credit default swap market, a relatively unknown market from the general public but for its role in the contagion of bank failures during the global financial crisis of 2007-2008, while introducing the datasets that have been used in the empirical studies. Unlike the existing body of literature which mostly offers descriptive studies, we aim at building models and large information systems based on clusters which are seen as basic building blocks: These foundations must be stable. That is why the work undertaken and described in the following intends to ground further the clustering methodologies. For that purpose, we discuss their consistency and propose alternative measures of similarity that can be plugged in the clustering methodologies. We study empirically their impact on the clusters. Results of the empirical studies can be explored at www.datagrapple.com.
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Gautier Marti. Some contributions to the clustering of financial time series and applications to credit default swaps. Machine Learning [cs.LG]. Université Paris-Saclay, 2017. English. ⟨NNT : 2017SACLX097⟩. ⟨tel-01684941⟩

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