论文标题
动态COVAR建模和估计
Dynamic CoVaR Modeling and Estimation
论文作者
论文摘要
流行的系统性风险措施Covar（有条件的危险价值）及其变体广泛用于经济和金融中。在本文中，我们提出了价值风险（VAR）和COVAR的联合动态预测模型。我们认为的COVAR版本定义为一个有条件的一个大量变量（例如金融系统中的损失），这是在其他一些变量（例如，银行股份中的损失）处于困境中。我们为模型参数介绍了一个两步M-静态器，该模型参数借鉴了该对的最近提出的双变量评分函数（VAR，COVAR）。我们证明了参数估计器的一致性和渐近正态性，并分析了其在模拟中的有限样本特性。最后，我们将我们称为Cocaviar模型的动态预测模型的特定子类应用于美国大型银行的记录。正式的预测比较表明，我们的Cocaviar模型产生的COVAR预测优于当前基准模型发布的预测。
The popular systemic risk measure CoVaR (conditional Value-at-Risk) and its variants are widely used in economics and finance. In this article, we propose joint dynamic forecasting models for the Value-at-Risk (VaR) and CoVaR. The CoVaR version we consider is defined as a large quantile of one variable (e.g., losses in the financial system) conditional on some other variable (e.g., losses in a bank's shares) being in distress. We introduce a two-step M-estimator for the model parameters drawing on recently proposed bivariate scoring functions for the pair (VaR, CoVaR). We prove consistency and asymptotic normality of our parameter estimator and analyze its finite-sample properties in simulations. Finally, we apply a specific subclass of our dynamic forecasting models, which we call CoCAViaR models, to log-returns of large US banks. A formal forecast comparison shows that our CoCAViaR models generate CoVaR predictions which are superior to forecasts issued from current benchmark models.
