Tail Approximation of Value-at-Risk under Multivariate Regular Variation
نویسندگان
چکیده
This paper presents a general tail approximation method for evaluating the Valueat-Risk of any norm of random vectors with multivariate regularly varying distributions. The main result is derived using the relation between the intensity measure of multivariate regular variation and tail dependence function of the underlying copula, and in particular extends the tail approximation discussed in Embrechts et al [6] for Archimedean copulas. The explicit tail approximations for random vectors with Archimedean copulas and multivariate Pareto distributions are also presented to illustrate the results.
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