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.
Description:
DATA AVAILABILITY STATEMENT : Data will be made available upon request from the authors, as underlying
data has been obtained from a subscription-based source. Computer codes are available at
https://pypi.org/project/lppls/ (accessed on 17 September 2023).