Network quantile autoregression

Xuening Zhu, Weining Wang, Hangsheng Wang and Wolfgang Härdle

No 2016-050, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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 autoregres- sion model (NQAR) to characterize the dynamic quantile behavior in a complex system. In particular, we relate responses to its connected nodes and node spe- ci c 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 in- vestigating the nancial contagions in the Chinese stock market accounting for shared ownership of companies.

Keywords: Social Network; Quantile Regression; Autoregression; Systemic Risk; Financial Contagion; Shared Ownership (search for similar items in EconPapers)
Date: 2016
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Journal Article: Network quantile autoregression (2019)
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