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Quantifying Market Sentiment Influence on Systemic Connectedness: A Quantile-Based Analysis Across Asset Classes
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Bull and bear markets Market sentiment Quantile connectedness Systemic risk analysis Volatility spillovers
Contexto Educativo
Citação
Almeida, D., Ferreira, P., Dionísio, A. et al. Quantificando a influência do sentimento de mercado na conectividade sistêmica: uma análise baseada em quantis em diferentes classes de ativos. Comput Econ (2026). https://doi.org/10.1007/s10614-026-11406-8