Abstract A novel dynamic model for joint estimation of multiple quantiles of a time series conditionally on a set of covariates is presented. The model preserves quantile monotonicity and allows for a clear interpretation of covariate effects across quantiles. Model parameters are estimated using a two-step M-estimator. The resulting estimator is consistent, and its finite sample properties are analysed through simulations. The new model is used to study the impact of different levels of stress in the financial system on GDP growth rate. The analysis shows that worsened financial conditions imply a more pessimistic economic outlook when the financial scenario is already severely distressed, and an overall increased macroeconomic uncertainty. Additionally, past information on GDP growth is found to be critical in studying and predicting economic vulnerability. These findings hold true even when alternative measures of real economic activity are considered.