Industry Interdependency Dynamics in a Network Context

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2017-012

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Large Data Sets: Modeling and Analysis ; Financial Econometrics ; Portfolio Choice; Investment Decisions ; Financial Forecasting and Simulation
• Subject: dynamic network ; interdependency ; general predictive model ; quantile LASSO ; connectedness ; centrality ; prediction accuracy ; network-based trading strategy

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Qian, Ya ; Härdle, Wolfgang Karl ; Chen, Cathy Yi-Hsuan
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2017

Further information

• Handle: http://hdl.handle.net/10419/169202
• Last update: 10.03.2025, 11:45 AM CET