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<p>Financial markets are becoming more interconnected, leading to a better understanding of how shocks can spread across different markets. Investor sentiment is crucial in market dynamics, especially during high volatility. However, traditional mean-based connectedness measures fail to capture asymmetric dependence and tail-risk dynamics and are therefore less suitable under extreme market conditions, such as crises or periods of heightened uncertainty. Against this backdrop, this study employs a quantile connectedness approach to analyze the transmission of shocks among ten financial indices, including a crypto assets-related index and an economic-news-related sentiment index. The results show significant changes in shock transmission patterns, with the Daily News Sentiment Index (DNSI) being the primary transmitter during bull markets, reinforcing the influence of news sentiment on investor behavior. Traditional indices like MSCI USA (MSCI_USA) and MSCI Europe (MSCI_EUR) are key transmitters in bear and normal market conditions, while MSCI China (MSCI_CN) and the Dow Jones Commodity Index (DJCI) are more sensitive to external shocks and act as net receivers. Furthermore, the dynamic analysis highlights the evolving role of sentiment-based indicators, particularly in extreme market regimes, supporting the role of sentiment-driven contagion effects and emphasizing their relevance for risk management. These findings highlight the importance of sentiment and traditional and emerging markets in risk management and portfolio diversification for investors and policymakers.</p>

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Quantifying Market Sentiment Influence on Systemic Connectedness: A Quantile-Based Analysis Across Asset Classes

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Dora Almeida,
  • Paulo Ferreira,
  • Andreia Dionísio,
  • Derick Quintino,
  • Faheem Aslam

摘要

Financial markets are becoming more interconnected, leading to a better understanding of how shocks can spread across different markets. Investor sentiment is crucial in market dynamics, especially during high volatility. However, traditional mean-based connectedness measures fail to capture asymmetric dependence and tail-risk dynamics and are therefore less suitable under extreme market conditions, such as crises or periods of heightened uncertainty. Against this backdrop, this study employs a quantile connectedness approach to analyze the transmission of shocks among ten financial indices, including a crypto assets-related index and an economic-news-related sentiment index. The results show significant changes in shock transmission patterns, with the Daily News Sentiment Index (DNSI) being the primary transmitter during bull markets, reinforcing the influence of news sentiment on investor behavior. Traditional indices like MSCI USA (MSCI_USA) and MSCI Europe (MSCI_EUR) are key transmitters in bear and normal market conditions, while MSCI China (MSCI_CN) and the Dow Jones Commodity Index (DJCI) are more sensitive to external shocks and act as net receivers. Furthermore, the dynamic analysis highlights the evolving role of sentiment-based indicators, particularly in extreme market regimes, supporting the role of sentiment-driven contagion effects and emphasizing their relevance for risk management. These findings highlight the importance of sentiment and traditional and emerging markets in risk management and portfolio diversification for investors and policymakers.