An Estimator about the Multivariate Copulas Density via Wavelet Local Threshold Method
In order to quantify the local characteristics of the dependency structure among assets, Wavelet threshold rules are introduced into Copula parameter estimation. This paper provides a local threshold estimator of the multivariate copulas density. It is shown that three important factors which have effect on the estimation precision are sample size, variable dimension and smoothness index of copula density, and the following simulation of normal Copula supports the result. Thus, our methods enhance the local self-adaptation of a parametric Copula and help to improve the valuation of Value-at-Risk and optimization of assets allocation.
NETL
NSTL
万方数据
维普
