英文标题：
《Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting
Incorporating Two-sided Weibull Distribution》
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作者：
Chao Wang (1), Qian Chen (2), Richard Gerlach (1) ((1) Discipline of
Business Analytics, The University of Sydney, (2) HSBC Business School,
Peking University)
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最新提交年份：
2017
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英文摘要：
The realized GARCH framework is extended to incorporate the two-sided Weibull distribution, for the purpose of volatility and tail risk forecasting in a financial time series. Further, the realized range, as a competitor for realized variance or daily returns, is employed in the realized GARCH framework. Further, sub-sampling and scaling methods are applied to both the realized range and realized variance, to help deal with inherent micro-structure noise and inefficiency. An adaptive Bayesian Markov Chain Monte Carlo method is developed and employed for estimation and forecasting, whose properties are assessed and compared with maximum likelihood, via a simulation study. Compared to a range of well-known parametric GARCH, GARCH with two-sided Weibull distribution and realized GARCH models, tail risk forecasting results across 7 market index return series and 2 individual assets clearly favor the realized GARCH models incorporating two-sided Weibull distribution, especially models employing the sub-sampled realized variance and sub-sampled realized range, over a six year period that includes the global financial crisis.
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中文摘要：
为了对金融时间序列中的波动性和尾部风险进行预测，将已实现的GARCH框架扩展为包含双边威布尔分布。此外，作为已实现方差或每日收益的竞争对手，已实现范围在已实现GARCH框架中使用。此外，对实现范围和实现方差都应用了次采样和缩放方法，以帮助处理固有的微观结构噪声和低效率。提出了一种自适应贝叶斯马尔可夫链蒙特卡罗方法，并将其用于估计和预测，通过仿真研究，对其性能进行了评估，并与最大似然法进行了比较。与一系列著名的参数化GARCH、双边威布尔分布GARCH和已实现GARCH模型相比，7个市场指数收益率序列和2个单项资产的尾部风险预测结果明显倾向于采用双边威布尔分布的已实现GARCH模型，尤其是采用次抽样已实现方差和次抽样已实现范围的模型，包括全球金融危机在内的六年期间。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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