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Geopolitical risks and investor sentiment: Causality and TVP-VAR analysis

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  • He, Zhifang

Abstract

This paper aims to explore the relationship between geopolitical risks (GPR) and investor sentiment in the US stock market based on Granger causality test and time-varying parameter vector autoregression (TVP-VAR) analysis. Empirical results indicate that changes in geopolitical risks can affect investor sentiment, whereas investor sentiment cannot affect geopolitical risks. More importantly, geopolitical risks have significant negative effects on investor sentiment, suggesting that higher (lower) geopolitical risks dampen (promote) investor sentiment directly or indirectly. Specifically, the negative effects of geopolitical risks show substantial time variation and generally decrease over time. The response of investor sentiment appears to be more pronounced in the short and medium term than in the long term, and is more sensitive to domestic geopolitical events. There is no significant difference in the impacts of geopolitical risks (GPR), geopolitical threats (GPT), and geopolitical acts (GPA). The results obtained are robust for alternative investor sentiment and geopolitical risk indicators.

Suggested Citation

  • He, Zhifang, 2023. "Geopolitical risks and investor sentiment: Causality and TVP-VAR analysis," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    • Handle: RePEc:eee:ecofin:v:67:y:2023:i:c:s1062940823000700
    DOI: 10.1016/j.najef.2023.101947
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    More about this item

    Keywords

    Geopolitical risk; Investor sentiment; TVP-VAR; Time-varying impact;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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