Stock market bubbles and the realized volatility of oil price returns

Rangan Gupta, Joshua Nielsen and Christian Pierdzioch

Energy Economics, 2024, vol. 132, issue C

Abstract: Using monthly data for the G7 countries from 1973 to 2020, we study whether stock market bubbles help to forecast out-of-sample the realized volatility of oil price returns. We use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify both positive and negative bubbles in the short-, medium, and long-term. First, we successfully detect major crashes and rallies using the MS-LPPLS-CIs. Having established the relevance of the bubbles indicators, and given the large number of them, we use widely-studied shrinkage (Lasso, elastic net, ridge regression) approaches to estimate our forecasting models. We find that stock market bubbles have predictive value for realized volatility at a short to intermediate forecast horizon. The number of bubble predictors included in the penalized forecasting models tend to increase in the forecast horizon. We obtain our main finding for the various types of stock market bubbles, and for good and bad realized volatilities.

Keywords: Realized volatility; Oil price; Stock market bubbles; Forecasting; Shrinkage estimators (search for similar items in EconPapers)
JEL-codes: C22 C53 G15 Q02 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Stock Market Bubbles and the Realized Volatility of Oil Price Returns (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:132:y:2024:i:c:s0140988324001403

DOI: 10.1016/j.eneco.2024.107432

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