论文标题
半参数动态有条件相关框架，以预测风险
A semi-parametric dynamic conditional correlation framework for risk forecasting
论文作者
论文摘要
我们为关节投资组合价值（VAR）和预期不足（ES）预测开发了一种新型的多元半参数框架。与现有的单变量半参数方法不同，所提出的框架明确模拟了投资组合资产之间通过动态条件相关（DCC）参数化返回的依赖性结构。为了估计模型，采用了基于严格一致的VAR和关节损耗函数的最小化的两步程序。此过程允许同时估计DCC参数和投资组合风险因素。从2016年12月至2021年9月，对道琼斯琼斯指数组件的预测研究进行了预测研究，从2016年12月至2021年9月进行了对道琼斯指数组成部分的预测研究，评估了拟议模型在各种概率水平上的性能。与现有方法相比，经验结果支持拟议框架的有效性。
We develop a novel multivariate semi-parametric framework for joint portfolio Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting. Unlike existing univariate semi-parametric approaches, the proposed framework explicitly models the dependence structure among portfolio asset returns through a dynamic conditional correlation (DCC) parameterization. To estimate the model, a two-step procedure based on the minimization of a strictly consistent VaR and ES joint loss function is employed. This procedure allows to simultaneously estimate the DCC parameters and the portfolio risk factors. The performance of the proposed model in risk forecasting on various probability levels is evaluated by means of a forecasting study on the components of the Dow Jones index for an out-of-sample period from December 2016 to September 2021. The empirical results support effectiveness of the proposed framework compared to a variety of existing approaches.
