Timely characterizations of risks in economic and financial systems play an essential role in both economic policy and private sector decisions. However, the informational content of low-frequency variables and the results from conditional mean models provide only limited evidence to investigate this problem. We propose a novel mixed-frequency quantile vector autoregression (MF-QVAR) model to address this issue. Inspired by the univariate Bayesian quantile regression literature, the multivariate asymmetric Laplace distribution is exploited under the Bayesian framework to form the likelihood. A data augmentation approach coupled with a precision sampler efficiently estimates the missing low-frequency variables at higher frequencies under the state-space representation. The proposed methods allow us to nowcast conditional quantiles for multiple variables of interest and to derive quantile-related risk measures at high frequency, thus enabling timely policy interventions. The main application of the model is to nowcast conditional quantiles of the US GDP, which is strictly related to the quantification of Value-at-Risk and the Expected Shortfall.
翻译:对经济和金融系统风险的及时定性在经济政策和私营部门决策中都起着关键作用。然而,低频率变量的信息内容和有条件平均模型的结果只能为调查这一问题提供有限的证据。我们提议了一种新的混合频度四分位矢量自动递减模式(MF-QVAR)来解决这一问题。受单一巴伊西亚四分位回归文献的启发,在巴伊西亚框架之下,多变不对称拉普尔分布被利用来形成可能性。数据增加方法加上精确的取样器有效估计了州空间代表制下高频率的缺失低频变量。拟议方法使我们能够现在为多种利益变量设定有条件的孔点,并得出高频率的四分位风险措施,从而使得能够及时采取政策干预。模型的主要应用是现在将美国国内生产总值的有条件的孔化,这严格地关系到价值的量化和预期矮值。