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Energy Market Uncertainties and US State-Level Stock Market Volatility: A GARCH-MIDAS Approach

Author

Listed:
  • Afees A. Salisu

    (Centre for Econometrics and Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Ahamuefula E.Oghonna

    (Centre for Econometrics and Applied Research, Ibadan, Nigeria)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Oguzhan Cepni

    (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Ostim Technical University, Ankara, Turkiye)

Abstract

In this paper, we employ the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of state-level stock returns in the United States (US) based on monthly metrics of oil price uncertainty (OPU), and relatively broader energy market-related uncertainty index (EUI). We find that over the daily period of (February) 1994 to (September) 2022 and various forecast horizons, in 37 out of the 50 states, the GARCH-MIDAS model with EUI can outperform the benchmark, i.e., the GARCH-MIDAS-realized volatility (RV), which in turn, holds for at most 18 cases under OPU. The statistical evidence is further strengthened when we are able to detect higher utlilty gains delivered for 42 states by the GARCH-MIDAS-EUI in comparison to the GARCH-MIDAS-RV. Our findings have important implications for investors and policymakers.

Suggested Citation

  • Afees A. Salisu & Ahamuefula E.Oghonna & Rangan Gupta & Oguzhan Cepni, 2024. "Energy Market Uncertainties and US State-Level Stock Market Volatility: A GARCH-MIDAS Approach," Working Papers 202409, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202409
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    References listed on IDEAS

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    More about this item

    Keywords

    Monthly Oil Price and Energy Market Uncertainties; Daily State-Level Stock Returns Volatility; GARCH-MIDAS; Forecasting;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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