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Investigating extreme linkage topology in the aerospace and defence industry

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  • Bouri, Elie
  • Quinn, Barry
  • Sheenan, Lisa
  • Tang, Yayan

Abstract

This paper analyses return and volatility spillovers among 21 global aerospace and defence (A&D) companies from six countries and three continents using quantile-based models and daily data from August 23, 2010, to July 1, 2022. The results show that both return and volatility spillovers vary over time, and those estimated at normal market conditions, intensify during COVID-19 and Russia–Ukraine war periods. Spillovers of returns estimated at lower and upper quantiles exceed those estimated at the middle quantile. Volatility spillover is extremely high at the upper quantile and exhibits low variability. Chinese defence stocks are segmented from the rest under normal return conditions and a moderate volatility state. In contrast, they are somewhat integrated under extreme return conditions and volatility states. Hence, Chinese defence stocks entail more diversification benefits under normal conditions than in bear or bull markets. Further analysis shows that geopolitical risk consistently plays a significant role in driving both returns and volatility spillovers, especially during the pandemic and war periods, without ignoring the role of macroeconomic and financial variables. These results have implications for investors concerned with stock portfolio management under various return and volatility conditions and for policymakers preoccupied with policy design under unstable periods

Suggested Citation

  • Bouri, Elie & Quinn, Barry & Sheenan, Lisa & Tang, Yayan, 2024. "Investigating extreme linkage topology in the aerospace and defence industry," International Review of Financial Analysis, Elsevier, vol. 93(C).
  • Handle: RePEc:eee:finana:v:93:y:2024:i:c:s105752192400098x
    DOI: 10.1016/j.irfa.2024.103166
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    References listed on IDEAS

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    More about this item

    Keywords

    Aerospace and defence companies; Ukrainian war; Russia; Quantile vector-autoregression (QVAR); COVID-19;
    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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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