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Time-varying spillover of multi-scale positive and negative bubbles in stock and oil markets

Author

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  • Foglia, Matteo
  • Gupta, Rangan
  • Caraiani, Petre
  • Pacelli, Vincenzo

Abstract

The objective of this paper is to analyze time-varying spillover between bubbles in oil and stock markets of the U.S. In this regard, 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 in the two markets. In the second-step, we utilize a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model to conduct the spillover analysis among the indexes of oil and stock positive and negative bubbles. Based on data covering the monthly period of January 1999 to June 2025, we find that negative bubble spillovers are significantly stronger and more directional than positive ones, with the U.S. equity market emerging as the transmitter to the oil market post-2008. This represents a structural shift from the traditional oil-to-equity transmission paradigm. Moreover, spillover effects are most pronounced at short- and medium-term horizons, intensifying during crisis periods. Our findings suggest that oil is increasingly behaving as a financial asset rather than a physical commodity, with important implications for portfolio diversification and risk management.

Suggested Citation

  • Foglia, Matteo & Gupta, Rangan & Caraiani, Petre & Pacelli, Vincenzo, 2026. "Time-varying spillover of multi-scale positive and negative bubbles in stock and oil markets," Finance Research Letters, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:finlet:v:88:y:2026:i:c:s1544612325024286
    DOI: 10.1016/j.frl.2025.109179
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    Cited by:

    1. Ufuk Can & Oguzhan Cepni & Rangan Gupta & Onur Polat, 2026. "From Supply-Chain Disruptions to Speculative Exuberance: How Energy Transportation Uncertainty Drives Oil Price Bubbles," Working Papers 202608, University of Pretoria, Department of Economics.
    2. Onur Polat & Rangan Gupta & Dhanashree Somani & Sayar Karmakar, 2026. "Machine Learning Forecasting of U.S. Stock Market Volatility: The Role of Stock and Oil Bubbles," Working Papers 202611, University of Pretoria, Department of Economics.

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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