ABSTRACT
Purpose: This paper investigates the influence of three different sentiment indicators on the time-varying stock–bond correlation of 15 countries during the global crisis period of the coronavirus disease 2019 (COVID-19) pandemic. Design/methodology/approach: The author uses the time-varying correlation estimated using the autoregressive moving average -dynamic conditional correlation - generalised autoregressive conditional heteroskedasticity (ARMA-DCC-GARCH) model to achieve this aim. The impact of investor sentiment on the stock–bond correlation was analysed using the Markov regime-switching regression. Findings: The study results show that the sentiment indicators of fear, uncertainty and distress have a pronounced negative impact on the stock–bond correlation. They further provide evidence of a strong regime effect on the stock–bond correlation with sentiment indicators. Practical implications: The paper has a relevant impact on policymakers and fund managers. First, the policymakers now have more insightful evidence of how the stock and bond markets react during crises. Second, the fund managers need to focus on behavioural variables as they may be driving factors in crisis periods that may impair portfolio management. Originality/value: To the best of my knowledge, the paper is the first to throw light on the behaviour of the stock–bond correlation for 15 countries during the COVID-19 period. © 2022, Emerald Publishing Limited.