Semiparametric estimation in copula models
β Scribed by Hideatsu Tsukahara
- Publisher
- John Wiley and Sons
- Year
- 2005
- Tongue
- French
- Weight
- 931 KB
- Volume
- 33
- Category
- Article
- ISSN
- 0319-5724
No coin nor oath required. For personal study only.
β¦ Synopsis
The author recalls the limiting behaviour of the empirical copula process and applies it to prove some asymptotic properties of a minimum distance estimator for a Euclidean parameter in a copula model. The estimator in question is semiparametric in that no knowledge of the marginal distributions is necessary. The author also proposes another semiparametric estimator which he calls "rank approximate Z-estimator" and whose asymptotic normality he derives. He further presents Monte Carlo simulation results for the comparison of various estimators in four well-known bivariate copula models.
Cestimation semiparametrique dans les modeles de copules Rksurnk : L'auteur rappelle le comportement limite du processus de copule empirique et en d6duit certaines propriCt6s asymptotiques d'un estimateur ? I distance minimale d'un parametre euclidien dans un modele de copules. L'estimateur en question est semiparamCtrique en ce qu'il ne depend pas des marges. L'auteur propose un autre estimateur semiparamktrique dit "Z-estimateur de rang approch6," dont il d6montre la normalit6 asymptotique. Il pdsente en outre les r6sultats de simulations de Monte-Carlo visant ? I comparer divers estimateurs dans le cadre de quatre modBles de copules bivari6s bien connus.
π SIMILAR VOLUMES
## SUMMARY We propose a new dynamic copula model in which the parameter characterizing dependence follows an autoregressive process. As this model class includes the Gaussian copula with stochastic correlation process, it can be viewed as a generalization of multivariate stochastic volatility model