SFB649DP2016 050
Network Quantile Autoregression
Xuening Zhu
Weining Wang
Hangsheng Wang
Wolfgang K. Härdle
Abstract:
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.
Keywords:
Social Network, Quantile Regression, Autoregression, Systemic Risk,
Financial Contagion, Shared Ownership
JEL Classification:
C12, C22