SFB649DP2017 012
Industry Interdependency Dynamics in a Network Context
Ya Qian
Wolfgang Karl Härdle
Cathy Yi-Hsuan Chen
Abstract:
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.
Keywords:
dynamic network, interdependency, general predictive model, quantile LASSO,
connectedness, centrality, prediction accuracy, network-based trading strategy
JEL Classification:
C32, C55, C58, G11, G17