Dynamic Network Quantile Regression Model

Abstract

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.

Keywords: Dynamic Network Quantile Regression Model, Simultaneous Network Endogeneity, IVQR Estimator, Quantile Connectedness

JEL Classification: C32, C51, G17