Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/329304 
Year of Publication: 
2025
Citation: 
[Journal:] Economies [ISSN:] 2227-7099 [Volume:] 13 [Issue:] 2 [Article No.:] 24 [Year:] 2025 [Pages:] 1-25
Publisher: 
MDPI, Basel
Abstract: 
While there is a large body of literature on oil uncertainty-equity prices and/or returns nexus, an associated important question of how oil market uncertainty affects stock market bubbles remains unanswered. In this paper, we first use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium- and long-term stock markets of the G7 countries. While detecting major crashes and booms in the seven stock markets over the monthly period of February 1973 to May 2020, we also observe similar timing of strong (positive and negative) LPPLS-CIs across the G7, suggesting synchronized boom-bust cycles. Given this, we next apply dynamic heterogeneous coefficients panel databased regressions to analyze the predictive impact of a model-free robust metric of oil price uncertainty on the bubbles indicators. After controlling for the impacts of output growth, inflation, and monetary policy, we find that oil price uncertainty predicts a decrease in all the time scales and countries of the positive bubbles and increases strongly in the medium term for five countries (and weakly the short-term) negative LPPLS-CIs. The aggregate findings continue to hold with the inclusion of investor sentiment indicators. Our results have important implications for both investors and policymakers, as the higher (lower) oil price uncertainty can lead to a crash (recovery) in a bullish (bearish) market.
Subjects: 
multi-scale bubbles
oil price uncertainty
panel data regressions
G7 stock markets
JEL: 
C22
C32
C33
G15
Q02
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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