Network Quantile Autoregression

SFB 649 Discussion Paper 2016-050

56 Pages Posted: 23 Nov 2016

See all articles by Xuening Zhu

Xuening Zhu

Peking University

Weining Wang

Humboldt University of Berlin

Hangsheng Wang

Peking University

Wolfgang K. Härdle

Humboldt University of Berlin - Institute for Statistics and Econometrics; Xiamen University - Wang Yanan Institute for Studies in Economics (WISE); Charles University; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

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Date Written: November 23, 2016

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

Suggested Citation

Zhu, Xuening and Wang, Weining and Wang, Hangsheng and Härdle, Wolfgang K., Network Quantile Autoregression (November 23, 2016). SFB 649 Discussion Paper 2016-050. Available at SSRN: https://ssrn.com/abstract=2874686 or http://dx.doi.org/10.2139/ssrn.2874686

Xuening Zhu

Peking University ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China

Weining Wang

Humboldt University of Berlin ( email )

Unter den Linden 6
Berlin, AK Berlin 10099
Germany

Hangsheng Wang

Peking University

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China

Wolfgang K. Härdle (Contact Author)

Humboldt University of Berlin - Institute for Statistics and Econometrics ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Xiamen University - Wang Yanan Institute for Studies in Economics (WISE) ( email )

A 307, Economics Building
Xiamen, Fujian 10246
China

Charles University ( email )

Celetná 13
Dept Math Physics
Praha 1, 116 36
Czech Republic

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

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