Network quantile autoregression

Xuening Zhu, Weining Wang, Hansheng Wang and Wolfgang Karl Härdle

Journal of Econometrics, 2019, vol. 212, issue 1, 345-358

Abstract: The complex tail dependency structure in a dynamic network with a large number of nodes is an important object to study. We propose a network quantile autoregression model (NQAR), which characterizes the dynamic quantile behavior. Our NQAR model consists of a system of equations, of which we relate a response to its connected nodes and node specific characteristics in a quantile autoregression process. We show the estimation of the NQAR model and the asymptotic properties with assumptions on the network structure. For this propose we develop a network Bahadur representation that gives us direct insight into the parameter asymptotics. Moreover, innovative tail-event driven impulse functions are defined. Finally, we demonstrate the usage of our model by investigating the financial contagions in the Chinese stock market accounting for shared ownership of companies. We find higher network dependency when the market is exposed to a higher volatility level.

Keywords: Social network; Quantile regression; Autoregression; Systemic risk; Financial contagion; Shared ownership (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (30)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:212:y:2019:i:1:p:345-358

DOI: 10.1016/j.jeconom.2019.04.034

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