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<p>This study investigates the dynamic evolution of long-memory properties and network interdependencies across stock market indices of E-7 and G-7 economies from January 2000 to June 2024. We employ multiple long-memory estimators with time-varying analysis to examine market efficiency dynamics through the Adaptive Market Hypothesis lens. Our approach utilizes the theoretically rigorous lifting wavelet-based estimator within a rolling window framework to capture temporal variations in the Hurst exponent, subsequently constructing networks via Dynamic Time Warping techniques to identify shifts in market interdependencies, with centrality measures employed to assess the structural significance of individual indices. Our empirical results reveal shifts in long-memory behaviour across most stock market indices, with a pronounced upward trend following the Global Financial Crisis (2008–2009) and during the European Sovereign Debt Crisis (2010–2012). A similar pattern is observed in several indices during the COVID-19 period. Notably, the time-varying Hurst exponent for IMOEX indicates a transition from anti-persistent to persistent behaviour during the Russia–Ukraine war. Moreover, we observed substantial changes in long-memory community network and network centrality structures corresponding to various financial crises. These results yield important implications for both policymakers and market participants, offering valuable insights for the development of risk management strategies and portfolio optimization techniques, particularly during periods of financial market turbulence.</p>

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Crisis, Connectivity, and Market Efficiency: Dynamic Long-memory Networks of G7 and E7 Economies

  • 期刊论文

  • 发表日期：

    2025年10月10日

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  • Hitesh Kumar Sahu,
  • Avishek Bhandari

摘要

This study investigates the dynamic evolution of long-memory properties and network interdependencies across stock market indices of E-7 and G-7 economies from January 2000 to June 2024. We employ multiple long-memory estimators with time-varying analysis to examine market efficiency dynamics through the Adaptive Market Hypothesis lens. Our approach utilizes the theoretically rigorous lifting wavelet-based estimator within a rolling window framework to capture temporal variations in the Hurst exponent, subsequently constructing networks via Dynamic Time Warping techniques to identify shifts in market interdependencies, with centrality measures employed to assess the structural significance of individual indices. Our empirical results reveal shifts in long-memory behaviour across most stock market indices, with a pronounced upward trend following the Global Financial Crisis (2008–2009) and during the European Sovereign Debt Crisis (2010–2012). A similar pattern is observed in several indices during the COVID-19 period. Notably, the time-varying Hurst exponent for IMOEX indicates a transition from anti-persistent to persistent behaviour during the Russia–Ukraine war. Moreover, we observed substantial changes in long-memory community network and network centrality structures corresponding to various financial crises. These results yield important implications for both policymakers and market participants, offering valuable insights for the development of risk management strategies and portfolio optimization techniques, particularly during periods of financial market turbulence.