Network quantile autoregression

By Xuening Zhu, Weining Wang, Hangsheng Wang, Wolfgang Karl Härdle. Journal of Econometrics, 2019

87 citations recorded by the source corpus.

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.

Subfields: Condensed Matter Physics, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2c56da4cde617d330a5721fcc9844829c5fc415e

Citations in the corpus

• Quantile coherency: A general measure for dependence between cyclical economic variables (2015, unknown): “Motivated by the estimation method in Zhu et al. (2018), we compute ˆ θ ( τ ) = arg min τ ∈ T := { 1 / 50 , 2 / 50 , . . . , 48 / 50 , 49 / 50 } , where ρ τ ( u ) := u ( τ − I { u < τ } ) is the check function (cf. Koenker (2005)).”