Network quantile autoregression

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: SFB 649 Discussion Paper ; No. 2016-050

Klassifikation und Themen

• Klassifikation: Wirtschaft
• Thema: Social Network ; Quantile Regression ; Autoregression ; Systemic Risk ; Financial Contagion ; Shared Ownership

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Zhu, Xuening ; Wang, Weining ; Wang, Hangsheng ; Härdle, Wolfgang Karl
• Ereignis: Veröffentlichung
• (wer): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (wo): Berlin
• (wann): 2016

Weitere Informationen

• Handle: http://hdl.handle.net/10419/148885
• Letzte Aktualisierung: 10.03.2025, 11:45 MEZ