Fear sentiment spillovers and G7 market dynamics: evidence from a global crisis

Purpose The paper aims to examine the return spillover effects between G7 stock markets, investors' FEAR and pandemic-related CORONA FEAR while analyzing their correlation, volatility influence and dynamic interconnections. Design/methodology/approach The study uses daily data from January 2, 2019, to April 2021. Two novel sentiment indices (FEAR and CORONA FEAR) are constructed from Google Trends following Da et al. (2015). The analysis relies on the dynamic conditional correlation generalized autoregressive conditional heteroskedasticity model to capture dynamic correlations and the time-varying parameter vector autoregression (TVP-VAR) framework to assess time-varying connectedness across markets and fear indices. Findings Both FEAR and CORONA FEAR show long-run dynamic correlations with G7 stock markets. Uncertainty and pandemic fear gradually affect asset prices. TVP-VAR results reveal synchronization between fear sentiment and volatility across G7 markets. Both indices act as net volatility receivers, indicating stock markets are the main triggers of fear. Findings highlight the psychological impact of crises on investors and provide policy insights to reduce fear-driven reactions and enhance financial stability. Originality/value The paper introduces two new fear sentiment indices, FEAR and CORONA FEAR, derived from Google Trends data, and integrates them into financial spillover analysis, highlighting their role in shaping market volatility and investor behavior during crises.