caa a22 4500 606196366 CHVBK 20210128100924.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s10260-014-0274-7 doi (NATIONALLICENCE)springer-10.1007/s10260-014-0274-7 Nonparametric estimation of general multivariate tail dependence and applications to financial time series [Elektronische Daten] [Yuri Salazar, Wing Ng] In order to analyse the entire tail dependence structure among random variables in a multidimensional setting, we present and study several nonparametric estimators of general tail dependence functions. These estimators measure tail dependence in different orthants, complementing the commonly studied positive (lower and upper) tail dependence. This approach is in line with the parametric analysis of general tail dependence. Under this unifying approach the different dependencies are analysed using the associated copulas. We generalise estimators of the lower and upper tail dependence coefficient to the general multivariate tail dependence function and study their statistical properties. Tail dependence measures come as a response to the incapability of the correlation coefficient as an extreme dependence measure. We run a Monte Carlo simulation study to assess the performance of the nonparametric estimators. We also employ selected estimators in two empirical applications to detect and measure the general multivariate non-positive tail dependence in financial data, which popular parametric copula models commonly applied in the financial literature fail to capture. Springer-Verlag Berlin Heidelberg, 2014 Tail dependence nationallicence Copula nationallicence Nonparametric estimation nationallicence Financial asset returns nationallicence Salazar Yuri Centre for Financial Risk, Faculty of Business and Economics, Macquarie University, 2109, Sydney, NSW, Australia aut Ng Wing Centre for Computational Finance and Economic Agents (CCFEA), University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK aut Statistical Methods & Applications Springer Berlin Heidelberg 24/1(2015-03-01), 121-158 1618-2510 24:1<121 2015 24 10260 https://doi.org/10.1007/s10260-014-0274-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10260-014-0274-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Salazar Yuri Centre for Financial Risk, Faculty of Business and Economics, Macquarie University, 2109, Sydney, NSW, Australia aut NATIONALLICENCE 700 1- Ng Wing Centre for Computational Finance and Economic Agents (CCFEA), University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK aut NATIONALLICENCE 773 0- Statistical Methods & Applications Springer Berlin Heidelberg 24/1(2015-03-01), 121-158 1618-2510 24:1<121 2015 24 10260