论文标题
使用贝叶斯实现的Egarch模型预测尾巴风险
Tail risk forecasting using Bayesian realized EGARCH models
论文作者
论文摘要
本文为实现的指数广义自动回归有条件异质性（已实现的Egarch）模型开发了贝叶斯框架，该模型可以结合多个实现的返回系列建模的多次实现的波动率指标。实现的Egarch模型通过采用标准化的学生T和标准化的偏斜学生-T分布来扩展。在论文中考虑了不同类型的已实现措施，例如以下的实现方差，次采样的实现范围和已实现的内核。贝叶斯马尔可夫链蒙特卡洛（MCMC）估计在燃烧期间采用鲁棒的自适应大都市算法（RAM），在样本期间使用标准的随机步行大都市。在仿真研究中，贝叶斯估计器比最大似然估计器显示出更有利的结果。我们用几个指数测试了提出的模型，以预测1000天内的风险（VAR）和预期短缺（ES）的一步值。严格的尾巴风险预测评估表明，与一系列模型相比，采用标准化偏斜的学生-T分布并结合了子采样的实现范围的已实现的Egarch模型受到青睐。
This paper develops a Bayesian framework for the realized exponential generalized autoregressive conditional heteroskedasticity (realized EGARCH) model, which can incorporate multiple realized volatility measures for the modelling of a return series. The realized EGARCH model is extended by adopting a standardized Student-t and a standardized skewed Student-t distribution for the return equation. Different types of realized measures, such as sub-sampled realized variance, sub-sampled realized range, and realized kernel, are considered in the paper. The Bayesian Markov chain Monte Carlo (MCMC) estimation employs the robust adaptive Metropolis algorithm (RAM) in the burn in period and the standard random walk Metropolis in the sample period. The Bayesian estimators show more favourable results than maximum likelihood estimators in a simulation study. We test the proposed models with several indices to forecast one-step-ahead Value at Risk (VaR) and Expected Shortfall (ES) over a period of 1000 days. Rigorous tail risk forecast evaluations show that the realized EGARCH models employing the standardized skewed Student-t distribution and incorporating sub-sampled realized range are favored, compared to a range of models.
