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Evaluates the upper and lower tail dependence coefficients for the bivariate Extremal Skew-$t$ model.
chi.extst(corr=0, shape=rep(0,2), df=1, tail="upper")
Returns a value that is strictly greater than \(0\) and less than \(1\).
the correlation parameter, between \(-1\) and \(1\).
a numeric skewness vector of length \(2\).
a single positive value representing the degree of freedom.
the string "upper" or "lower".
"upper"
"lower"
Simone Padoan, simone.padoan@unibocconi.it, https://mypage.unibocconi.it/simonepadoan/; Boris Beranger, borisberanger@gmail.com https://www.borisberanger.com/;
Padoan, S. A. (2011). Multivariate extreme models based on underlying skew-t and skew-normal distributions. Journal of Multivariate Analysis, 102(5), 977-991.
### Upper tail dependence chi.extst(corr=0.5, shape=c(1,-2), df=2, tail="upper") ### Lower tail dependence chi.extst(corr=0.5, shape=c(1,-2), df=2, tail="lower")
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