Abstract
We explore the role of sequences of extreme values for measuring tail-dependence between times series. The proposed measure concentrates on searching extreme cause-effect fluctuation pairs in the recent time interval and requires much less data than current causality and dependence approaches. The target applications of this approach are those in which there is the necessity of rapidly recognizing the interval time in which a time series may be influenced by other time series characterized by sudden and unpredictable extreme changes. This paper presents the tail-dependence measure in the field of stock markets and compares it to known causality and dependence measures. An application of the mentioned measure in the field of space physics is also presented.
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Núñez, M., Morales, R. (2006). Extreme Value Dependence in Problems with a Changing Causation Structure. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_98
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DOI: https://doi.org/10.1007/11811305_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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