  We propose a dynamic network quantile regression model to investigate the
quantile connectedness using a predetermined network information. We extend the
existing network quantile autoregression model of Zhu et al. (2019b) by
explicitly allowing the contemporaneous network effects and controlling for the
common factors across quantiles. To cope with the endogeneity issue due to
simultaneous network spillovers, we adopt the instrumental variable quantile
regression (IVQR) estimation and derive the consistency and asymptotic
normality of the IVQR estimator using the near epoch dependence property of the
network process. Via Monte Carlo simulations, we confirm the satisfactory
performance of the IVQR estimator across different quantiles under the
different network structures. Finally, we demonstrate the usefulness of our
proposed approach with an application to the dataset on the stocks traded in
NYSE and NASDAQ in 2016.
