 A probabilistic clustering method for US interest rate analysisQuantitative Finance, 2012, vol. 12, issue 1, 135-148 Abstract: Finite mixture distributions provide a flexible method for high-dimensional data modeling. They are widely used in many disciplines such as astronomy and genetics. One reason for their popularity is their flexibility and straightforward implementation. The interest increase in multivariate mixtures and their applicability when they are combined with clustering methods motivated us to opt for these methods to analyse financial markets' dynamics. An empirical investigation of a set of interest rate time series is performed using a new methodology. The objective of these analyses is to determine the similarities and specificities among the analysed financial time series. Interest rates are subsequently classified according to their behavior. Such a study has been widely exploited for stock price changes using traditional methods, whereas interest rates have been less considered. The major advantage of cluster analysis is that it gives the study more realism, since it represents a functional relationship between independent and dependent variables. The EM algorithm improves clustering in the sense that it considers a stochastic relationship among variables taking missing data into account. Date: 2012 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:12:y:2012:i:1:p:135-148 Ordering information: This journal article can be ordered from DOI: 10.1080/14697681003591712 Access Statistics for this article Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral More articles in Quantitative Finance from Taylor & Francis Journals |
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