Semiparametric estimation and model selection for conditional mixture copula models
Semiparametric estimation and model selection for conditional mixture copula models
复制标题
条件混合Copula模型的半参数估计和模型选择
DOI：
10.1111/sjos.12514
复制
发表时间：
2021-03-03
影响因子：
1
通讯作者：
Cai, Zongwu
中科院分区：
文献类型：
--
作者：
Liu, Guannan;Long, Wei;Cai, Zongwu
Conditional copula models allow the dependence structure among variables to vary with covariates, and thus can describe the evolution of the dependence structure with those factors. This paper proposes a conditional mixture copula which is a weighted average of several individual conditional copulas. We allow both the weights and copula parameters to vary with a covariate so that the conditional mixture copula offers additional flexibility and accuracy in describing the dependence structure. We propose a two‐step semi‐parametric estimation method and develop asymptotic properties of the estimators. Moreover, we introduce model selection procedures to select the component copulas of the conditional mixture copula model. Simulation results suggest that the proposed procedures have a good performance in estimating and selecting conditional mixture copulas with different model specifications. The proposed model is then applied to investigate how the dependence structures among international equity markets evolve with the volatility in the exchange rate markets.
