City Research Online

Abstract

The complex tail dependency structure in a dynamic network with a large num- ber of nodes is an important object to study. Here 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.

Publication Type:	Article
Additional Information:	© 2018 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
Publisher Keywords:	Social Network; Quantile Regression; Autoregression; Systemic Risk; Financial Contagion; Shared Ownership.
Departments:	School of Policy & Global Affairs > Economics
SWORD Depositor:	Symplectic Administrator

Export

Downloads