Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

SFB649DP2016 050

Network Quantile Autoregression

Xuening Zhu
Weining Wang
Hangsheng Wang
Wolfgang K. Härdle

It is a challenging task to understand the complex dependency structures in an ultra-high dimensional network, especially when one concentrates on the tail dependency. To tackle this problem, we consider a network quantile autoregression
model (NQAR) to characterize the dynamic quantile behavior in a complex system. In particular, we relate responses to its connected nodes and node specific
characteristics in a quantile autoregression process. A minimum contrast estimation approach for the NQAR model is introduced, and the asymptotic properties are studied. Finally, we demonstrate the usage of our model by
investigating the financial contagions in the Chinese stock market accounting
for shared ownership of companies.

Social Network, Quantile Regression, Autoregression, Systemic Risk,
Financial Contagion, Shared Ownership

JEL Classification:
C12, C22