Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

SFB649DP2017 012

Industry Interdependency Dynamics in a Network Context

Ya Qian
Wolfgang Karl Härdle
Cathy Yi-Hsuan Chen

This paper contributes to model the industry interconnecting structure in a network context. General predictive model (Rapach et al. 2016) is extended to quantile LASSO regression so as to incorporate tail risks in the construction of industry
interdependency networks. Empirical results show a denser network with heterogeneous central industries in tail cases. Network dynamics demonstrate the variety of
interdependency across time. Lower tail interdependency structure gives the most accurate out-of-sample forecast of portfolio returns and network centrality-based trading strategies seem to outperform market portfolios, leading to the possible ’too central to fail’ argument.

dynamic network, interdependency, general predictive model, quantile LASSO, connectedness, centrality, prediction accuracy, network-based trading strategy

JEL Classification:
C32, C55, C58, G11, G17