A Nonparametric Bayesian Estimator of Copula Density with Applications to Financial Market
Copula 密度的非参数贝叶斯估计及金融市场应用
We propose a nonparametric Bayesian estimator of copula density based on the Logistic Gaussian Process density estimation method. A Gaussian process prior with flexible mean and covariance functions is placed on the underlying latent function. To avoid the typical boundary issues in copula density estimation, a transformation approach is adopted, allowing the latent process to be defined on an unrestricted support and then back-transformed to obtain the posterior copula…