Network Quantile Autoregression
SFB 649 Discussion Paper 2016-050
56 Pages Posted: 23 Nov 2016
Date Written: November 23, 2016
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
Suggested Citation: Suggested Citation