Forecasting and stress testing with quantile vector autoregression

Abstract

SummaryA quantile vector autoregressive (VAR) model, unlike standard VAR, traces the interaction among the endogenous random variables at any quantile. Quantile forecasts are obtained by factorizing the joint distribution in a recursive structure but cannot be obtained from reduced form estimation. Identification strategies and structural quantile impulse response functions are derived as generalization of the VAR model. The model is estimated using real and financial variables for the euro area. The dynamic properties of the system change across quantiles. This is relevant for stress testing exercises, whose goal is to forecast the tail behavior of the economy when hit by large financial and real shocks.

Connectedness Between Gold-Backed Cryptocurrencies and the G7 Stock Market Indices During Global Crises: Evidence From the Quantile Vector Autoregression Approach

“…However, the fundamental benefit of Q-VAR is that it explains the spillover effects caused by severe market situations. Similarly, it displays the dynamic connectedness between variables at different quantiles, unlike the conventional VAR model that analyzes the conditional mean and overlooks the extreme connectedness effects (Ali et al, 2023;Chavleishvili & Manganelli, 2024). Moreover, Q-VAR makes evaluating and identifying nonlinear effects in data easier, giving researchers insights into the nonlinear dynamics that drive variable interactions (Koenker, 2005).…”