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Estimation of the Spectral Measure and the Tail Dependence Coefficient for Regular Varying Random Ve
Company: ETH Zentrum
Year Of Publication: 2002
Month Of Publication: October
Pages: 70
Download Count: 342
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Comment Num: 0
Language: EN
Who Can Read: Free
Date: 1-31-2007
Publisher: Administrator
The present thesis uses multivariate regular variation asa tool for modelling heavy-tailed multivariate phenomena. Extremalbehavior of such random vectors is characterized in terms of a tail indexand a spectral measure or equivalently in terms of a measure μ.An estimator of μ is proposed and proved consistent. Calibration ofthis estimator is achieved by exploiting a scaling property the measureμ possesses, an idea proposed by St˘aric˘a and extended in the presentthesis. A survey on convergence of this estimator is included. Applicationsinclude estimation of the spectral measure, conditional probabilityestimations for improbable events as well as estimation of the tail dependencecoefficient. The thesis is concluded with a case study on currencyexchange data and a brief discussion on how multivariate regular variationcompares to other approaches of multivariate extremal modelling,applicability of the model and topics of future research.
Wendin, Jonathan Sign in to follow this author
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