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The network algorithm of Mehta and Patel [ 1986] m currently the best general algorithm for computing exact probabilities in r x c contingency tables with fixed marginals. Given here are some improvements to the network algorithm which speed Its computational performance; and thus increases the size of problems which can be handled, The new code also(More)
We use the conditional distribution and conditional expectation of one random variable given the other one being large to capture the strength of dependence in the tails of a bivariate random vector. We study the tail behavior of the boundary conditional cumulative distribution function (cdf) and two forms of conditional tail expectation (CTE) for various(More)
Tail dependence and conditional tail dependence functions describe, respectively, the tail probabilities and conditional tail probabilities of a copula at various relative scales. The properties as well as the interplay of these two functions are established based upon their homogeneous structures. The extremal dependence of a copula, as described by its(More)
In Aas et al. (2009) and Aas and Berg (2009), it is shown that vine copulas constructed from bivariate t-copulas can provide better fits to multivariate financial asset return data. Several published articles indicate that for several assets there might be stronger tail dependence of returns in the joint lower tail than upper tail. We use vine copula models(More)