指数-伽马模型下在险价值度量的贝叶斯估计

Bayesian Estimation of Value at Risk Measure under Exponential-Gamma Models

VaR风险度量在金融、保险中有重要的应用.本文建立了贝叶斯模型,在某种损失函数下研究了VaR风险度量的贝叶斯估计.证明了指数-伽马分布下贝叶斯估计的强相合性和渐近正态性,最后利用数值模拟的方法验证了不同样本容量下估计的收敛速度.

VaR measure has important applications in finance and insurance practice. In this paper, the Bayesian models are established. Under some loss function, the Bayeian estimate of VaR is derived. In addition, we prove the strongly consistency and asymptotic normality for the Bayesian estimation of VaR under exponential-Gamma model. Finally, the numerical simulation is done to verify the convergence rate of the estimate of VaR with diffierent sample sizes.