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Industry Interdependency Dynamics in a Network Context

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  • Qian, Ya
  • Härdle, Wolfgang Karl
  • Chen, Cathy Yi-Hsuan

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.

Suggested Citation

  • Qian, Ya & Härdle, Wolfgang Karl & Chen, Cathy Yi-Hsuan, 2017. "Industry Interdependency Dynamics in a Network Context," SFB 649 Discussion Papers 2017-012, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  • Handle: RePEc:zbw:sfb649:sfb649dp2017-012
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    References listed on IDEAS

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    Cited by:

    1. Keungoui Kim & Dieter F. Kogler & Sira Maliphol, 2024. "Identifying interdisciplinary emergence in the science of science: combination of network analysis and BERTopic," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    2. Yaya Su & Zhehao Huang & Benjamin M. Drakeford, 2019. "Monetary Policy, Industry Heterogeneity and Systemic Risk—Based on a High Dimensional Network Analysis," Sustainability, MDPI, vol. 11(22), pages 1-15, November.

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    More about this item

    Keywords

    dynamic network; interdependency; general predictive model; quantile LASSO; connectedness; centrality; prediction accuracy; network-based trading strategy;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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