Industry Interdependency Dynamics in a Network Context

Ya Qian, Wolfgang Härdle and Cathy Yi-Hsuan Chen

No 2017-012, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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 (search for similar items in EconPapers)
JEL-codes: C32 C55 C58 G11 G17 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/169202/1/SFB649DP2017-012.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb649:sfb649dp2017-012

Access Statistics for this paper

More papers in SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().