A0472
Title: Maximum pseudo-likelihood estimation of copula models and moments of order statistics
Authors: Alexandra Dias - University of York (United Kingdom) [presenting]
Abstract: It has been shown that despite being consistent and, in some cases, efficient, maximum pseudo-likelihood (MPL) estimation for copula models overestimates the level of dependence, especially for small samples with low levels of dependence. This is especially relevant in finance and insurance applications when data is scarce. It is shown that the canonical MPL method uses the mean of order statistics, and the proposal is to use the median or the mode instead. It is shown that the MPL estimators proposed are consistent and asymptotically normal. In a simulation study, the finite sample performance of the proposed estimators is compared with that of the original MPL and the inversion method estimators based on Kendall's tau and Spearman's rho. In the results, the modified MPL estimators, especially the one based on the mode of the order statistics, have better finite sample performance both in terms of bias and mean square error. An application to general insurance data shows that the level of dependence estimated between different products can vary substantially with the estimation method used.
