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A Time Series Data Mining and Unobserved Component Modeling Approach to Credit Risk Correlation Modeling
Başlık:
A Time Series Data Mining and Unobserved Component Modeling Approach to Credit Risk Correlation Modeling
Yazar:
Wilcox, Bruce A., author.
ISBN:
9780438017740
Yazar Ek Girişi:
Fiziksel Tanımlama:
1 electronic resource (305 pages)
Genel Not:
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Advisors: Fumio Hamano Committee members: Chin Chang; Marina Chugunova; Henry Schellhorn.
Özet:
The objective of this thesis is to explore whether applying data mining methods from the fields of information systems and applied mathematics and state-space methods from the domain of systems engineering to the management of a portfolio of bonds or other financial obligations in a new way can provide new insights and tools for portfolio managers. Specifically, this thesis develops and applies time series clustering approaches with the intention of developing effective tools for diversification of bond portfolios.
Traditional time series clustering techniques are based on specifying a relevant distance metric that captures the similarity or dissimilarity between any pair of time series and then executing an optimization routine to cluster time series in a way that maximizes inter-cluster distances while minimizing intra-cluster distances. Since the mid-1990s there has been significant research into the identification and validation of such distance-based approaches with varying degrees of success. This project defines and tests three such approaches.
A recent and distinct approach to time series clustering known as model-based clustering is based on the specification and estimation of finite mixture models. This approach is based on fitting time series to generative models and the assignment of time series to these generative models. The approach of model-based clustering is to simultaneously fit both the generative model parameters and the allocations of elements to clusters to achieve the best probabilistic fit to the data observed. This thesis work represents the first known published definition and evaluation of model-based time series clustering using generative models that are specified in state space format.
These four clustering approaches were developed in an extensive Matlab environment generated as part of this thesis and evaluated using techniques of synthetic bond data generation with a known ground truth and by examining the performance of portfolios constructed from an extensive database of actual bond transactions. The new model-based clustering approach was found to have tangible benefits both in providing portfolio diversification and intuitive insight into portfolio allocation decisions.
Notlar:
School code: 0047
Tüzel Kişi Ek Girişi:
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Yer Numarası | Demirbaş Numarası | Shelf Location | Lokasyon / Statüsü / İade Tarihi |
---|---|---|---|
XX(679695.1) | 679695-1001 | Proquest E-Tez Koleksiyonu | Arıyor... |
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