用于风险预测的半参数动态条件相关框架
A semi-parametric dynamic conditional correlation framework for risk forecasting
AI总结：
本文提出一个多变量半参数框架，通过DCC参数化显式建模资产收益依赖结构，采用两步法基于联合损失函数同时估计DCC参数与风险因子，有效提升了投资组合VaR和ES的联合风险预测能力。
摘要：
我们开发了一个新颖的多变量半参数框架，用于联合预测投资组合的在险价值（VaR）和预期缺口（ES）。与现有的单变量半参数方法不同，该框架通过动态条件相关（DCC）参数化显式地对投资组合资产收益之间的依赖结构进行建模。为了估计模型，采用了基于严格一致的VaR和ES联合损失函数最小化的两步过程。该过程允许同时估计DCC参数和投资组合风险因子。通过在2016年12月至2021年9月的样本外期间对Dow Jones指数成分股进行预测研究，评估了所提出模型在各种概率水平下的风险预测性能。实证结果支持了与多种现有方法相比，所提出框架的有效性。
英文摘要：
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.
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