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 autoregression …

financial econometrics. We propose a Tail Event driven Network Quantile Regression (TENQR) model which addresses these three …) autoregression involving network effects quantified through an adjacency matrix. To reflect the nature and risk content of systemic … thus provides measures for the required level of additional loss absorbency. It is discovered that the network effect, as a …

network attention proxy, network degree, provides a robust and significant cross-sectional predictability of the monthly stock …

market segmentation. To investigate these effects, we build a timevarying network for cryptocurrencies, based on the … clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets …

structure of price interdependencies across stocks. For correct empirical network determination of such dynamic liquidity price … bootstrap procedure. We document the importance of LOB liquidity network spillovers even for a small blue-chip NASDAQ portfolio. …

This paper contributes to model the industry interconnecting structure in a network context. General predictive model … interdependency networks. Empirical results show a denser network with heterogeneous central industries in tail cases. Network … out-of-sample forecast of portfolio returns and network centrality-based trading strategies seem to outperform market …

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 …