Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/169202
Authors: 
Qian, Ya
Härdle, Wolfgang
Chen, Cathy Yi-Hsuan
Year of Publication: 
2017
Series/Report no.: 
SFB 649 Discussion Paper 2017-012
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.
Subjects: 
dynamic network
interdependency
general predictive model
quantile LASSO
connectedness
centrality
prediction accuracy
network-based trading strategy
JEL: 
C32
C55
C58
G11
G17
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.