Dynamic Network Quantile Regression Model

TL;DR

This paper introduces a dynamic network quantile regression model that captures quantile connectedness in networks, addressing endogeneity with IVQR estimation, and demonstrates its effectiveness through simulations and stock market data analysis.

Contribution

It extends existing network quantile autoregression models by incorporating contemporaneous effects and controlling for common factors, using IVQR for endogeneity correction.

Findings

IVQR estimator performs well across different quantiles

Model captures network spillovers effectively

Application reveals quantile connectedness in stock markets

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.