… Second, it admits trackable quantile dynamics, which facilitates to … a novel network quantile
model that characterizes the dynamic quantile behavior, which incorporates valuable network …

… quantile autoregression neural network (QARNN) model based on an artificial neural network
… used to explore potential nonlinear relationships among quantiles in time series data. By …

… Employing the unweighted adjacency matrix and network quantile autoregression method,
we introduce a novel approach to ranking the systemic importance of the institutions. This …

… the quantile connectedness using a predetermined network information. We extend the
existing network quantile autoregression … allowing the contemporaneous network effects and …

… We introduce autoregressive implicit quantile networks (AIQN), a fundamentally different
ap… , that implicitly captures the distribution using quantile regression. AIQN is able to achieve …

… a dynamic spatial autoregressive quantile model. Using predetermined network information,
we study dynamic tail event driven risk using a system of conditional quantile equations. …

… This paper introduces network structure and a spatial quantile autoregressive model to
study the heterogeneous effect of exogenous variables on the different quantiles of company …

… network quantile (TGNQ) autoregression model for time series collected on large-scale
networks, … To account for the directional influence among network nodes, each network node is …

We propose a novel approach to calibrate the conditional value-at-risk (CoVaR) of financial
institutions based on neural network quantile regression. Building on the estimation results, …

… networks of integer and continuous-valued data. The vector of high-dimensional responses,
measured on the nodes of a known network, … , logistic network models [63], network quantile …