Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2020 024

Dynamic Spatial Network Quantile Autoregression

Xiu Xu
Weining Wang
Yongcheol Shin

This paper proposes a dynamic spatial autoregressive quantile model. Using
predetermined network information, we study dynamic tail event driven risk using
a system of conditional quantile equations. Extending Zhu, Wang, Wang and Härdle
(2019), we allow the contemporaneous dependency of nodal responses by
incorporating a spatial lag in our model. For example, this is to allow a firm’s
tail behavior to be connected with a weighted aggregation of the simultaneous
returns of the other firms. In addition, we control for the common factor
effects. The instrumental variable quantile regressive method is used for our
model estimation, and the associated asymptotic theory for estimation is also
provided. Simulation results show that our model performs well at various
quantile levels with different network structures, especially when the node size
increases. Finally, we illustrate our method with an empirical study. We uncover
significant network effects in the spatial lag among financial institutions.

Network, Quantile autoregression, Instrumental variables, Dynamic models

JEL Classification:
C32, C51, G17