Machine Learning Forecasting of U.S. Stock Market Volatility: The Role of Stock and Oil Bubbles

Onur Polat (), Rangan Gupta (), Dhanashree Somani () and Sayar Karmakar ()
Additional contact information
Onur Polat: Institute of Informatics, Hacettepe University, Beytepe Campus, 06800 Cankaya, Ankara, Turkiye
Rangan Gupta: Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
Dhanashree Somani: Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA
Sayar Karmakar: Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA

No 202611, Working Papers from University of Pretoria, Department of Economics

Abstract: This study examines the predictive power of multi-scale positive and negative speculative bubbles in equity and energy markets for S&P 500 realized variance across horizons from 1 to 24 months. Using a hierarchical modeling framework and machine learning estimators, the analysis evaluates whether stock and oil bubbles provide incremental information beyond macroeconomic variables and financial uncertainty. Applying Clark and West's (2007) tests for nested model comparisons, the results reveal a hierarchy in predictive content that varies by forecast horizon. At the 1-month horizon, neither stock nor oil bubbles improves forecast accuracy. At the 3-month horizon, oil bubbles emerge as the dominant predictor; the Bayesian Regularized Neural Network (BRNN) estimator achieves a statistically significant improvement when oil bubbles are included with stock bubbles, resulting in a 30.7 percent reduction in mean squared error (MSE). At the 6-month horizon, stock bubbles become more important, with both the Gradient Boosting Machine (GBM) and BRNN estimators showing significant improvements. For longer horizons, oil bubbles remain relevant, but their predictive value depends on the estimator: BRNN captures oil bubble effects at 12 months, while GBM does so at 24 months. These findings highlight the importance of horizonspecific model selection and indicate a complex transmission of speculative shocks across asset classes.

Keywords: Stock Market Realized Variance; Stock and Oil Bubbles; Machine Learning; Forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 G10 Q51 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2026-04
New Economics Papers: this item is included in nep-ene, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://drupalwebprod-files.up.ac.za/Public/2026-0 ... DslJfGkuVYbMYQTTa5zf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202611

Access Statistics for this paper

More papers in Working Papers from University of Pretoria, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Rangan Gupta ().