IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/202327.html

Oil Shocks and State-Level Stock Market Volatility of the United States: A GARCH-MIDAS Approach

Author

Listed:
  • Afees A. Salisu

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

  • 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)

  • Petre Caraiani

    (Institute for Economic Forecasting, Romanian Academy, Romania; Bucharest University of Economic Studies, Romania)

Abstract

In this paper, we employ the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of state-level stock returns in the United States (US) based on structurally decomposed four monthly oil shocks associated with oil supply, global economic activity, oil consumption and oil inventory. We find that over the daily period of (February) 1994 to (December) 2022 and various forecast horizons, in 46 out of the 50 states, the GARCH-MIDAS model with at least one oil shock can outperform the benchmark, i.e., the GARCH-MIDAS-Realized Volatility (RV), with 24 states depicting the importance of all the four shocks. In general, oil market-specific shocks, whether supply or demand, tend to matter more than a global economic impact driving the oil market in forecasting volatility of regional stock returns across with the better forecasting performances related to states with higher CO2 emissions based on underlying energy consumption data. Our findings have important implications for investors and policymakers.

Suggested Citation

  • Afees A. Salisu & Rangan Gupta & Oguzhan Cepni & Petre Caraiani, 2023. "Oil Shocks and State-Level Stock Market Volatility of the United States: A GARCH-MIDAS Approach," Working Papers 202327, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202327
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2025. "Electricity Sales and Forecasting of Stock Market Realized Volatility: A State-Level Analysis of the United States," Working Papers 202540, University of Pretoria, Department of Economics.
    2. Matteo Bonato & Rangan Gupta & Christian Pierdzioch, 2024. "Do Shortages Forecast Aggregate and Sectoral U.S. Stock Market Realized Variance? Evidence from a Century of Data," Working Papers 202450, University of Pretoria, Department of Economics.
    3. 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.

    More about this item

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pre:wpaper:202327. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Rangan Gupta (email available below). General contact details of provider: https://edirc.repec.org/data/decupza.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.