Modelling dependence using skew t copulas: Bayesian inference and applications
偏斜 t Copula 的依赖建模：贝叶斯推断与应用AI 解读 ↓
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偏斜 t Copula 的依赖建模：贝叶斯推断与应用
- 研究问题
- 如何在高维、离散边际或边际与依赖参数联合估计的情形下，刻画变量间非对称及极端依赖并缓解估计困难？
- 方法与思路
- 作者构造偏斜 t copula，利用条件高斯表示开展 MCMC 贝叶斯推断，并应用于澳大利亚区域电价和网站有序暴露数据。
- 材料陈述的贡献
- 摘要称方法能够处理所述估计难点，并在两项应用中明显优于对称椭圆 copula；具体评价指标和改善幅度未说明。
- 阅读关注点
- 应核对边际分布与 copula 的联合设定、离散数据处理、MCMC 收敛，以及非对称和极端依赖的比较指标。
- 阅读边界
- 仅据所给 Crossref 摘要，无法判断高维可扩展性、先验敏感性或比较公平性；两项应用的优势不能自动推广。
原文摘要
Abstract We construct a copula from the skew t distribution of Sahu et al. ( 2003 ). This copula can capture asymmetric and extreme dependence between variables, and is one of the few copulas that can do so and still be used in high dimensions effectively. However, it is difficult to estimate the copula model by maximum likelihood when the multivariate dimension is high, or when some or all of the marginal distributions are discrete‐valued, or when the parameters in the marginal distributions and copula are estimated jointly. We therefore propose a Bayesian approach that overcomes all these problems. The computations are undertaken using a Markov chain Monte Carlo simulation method which exploits the conditionally Gaussian representation of the skew t distribution. We employ the approach in two contemporary econometric studies. The first is the modelling of regional spot prices in the Australian electricity market. Here, we observe complex non‐Gaussian margins and nonlinear inter‐regional dependence. Accurate characterization of this dependence is important for the study of market integration and risk management purposes. The second is the modelling of ordinal exposure measures for 15 major websites. Dependence between websites is important when measuring the impact of multi‐site advertising campaigns. In both cases the skew t copula substantially outperforms symmetric elliptical copula alternatives, demonstrating that the skew t copula is a powerful modelling tool when coupled with Bayesian inference. Copyright © 2010 John Wiley & Sons, Ltd.