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

affiliation not provided to SSRN; University of York

Hangsheng Wang

Peking University

Wolfgang Karl Härdle

Blockchain Research Center Humboldt-Universität zu Berlin; Charles University; National Yang Ming Chiao Tung University; Asian Competitiveness Institute

Multiple version iconThere are 2 versions of this paper

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, Weining and Wang, Hangsheng and Härdle, Wolfgang Karl, 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

affiliation not provided to SSRN

University of York ( email )

Department of Economics and Related Studies Univer
York, YO10 5DD
United Kingdom

Hangsheng Wang

Peking University

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

Wolfgang Karl Härdle (Contact Author)

Blockchain Research Center Humboldt-Universität zu Berlin ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Charles University ( email )

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

National Yang Ming Chiao Tung University ( email )

No. 1001, Daxue Rd. East Dist.
Hsinchu City 300093
Taiwan

Asian Competitiveness Institute ( email )

Singapore

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