  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.
