Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting tail risks of Monthly US GDP

TL;DR

This paper introduces a Bayesian mixed-frequency quantile VAR model to better estimate and nowcast tail risks of US GDP, enabling more timely economic risk assessments.

Contribution

It develops a novel MF-QVAR model that incorporates Bayesian quantile regression with mixed-frequency data, improving tail risk estimation for economic variables.

Findings

Effective nowcasting of US GDP tail risks.

Enhanced quantile-based risk measures at high frequency.

Improved policy decision support through timely risk assessment.

Abstract

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.