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Forecasting Oil and Stock Returns with a Qual VAR using over 150 Years of Data

Author

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  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

  • Mark E. Wohar

    () (College of Business Administration, University of Nebraska at Omaha, USA, and School of Business and Economics, Loughborough University, UK.)

Abstract

The extant literature suggests that oil price, stock price and economic activity are all endogenous and the linkages between these variables are nonlinear. Against this backdrop, the objective of this paper is to use a Qualitative Vector Autoregressive (Qual VAR) to forecast (West Texas Intermediate) oil and (S&P500) stock returns over a monthly period of 1884:09 to 2015:08, using an in-sample period of 1859:10-1884:08. Given that there is no data on economic activity at monthly frequency dating as far back as 1859:09, we measure the same using the NBER recession dummies, which in turn, can be easily accommodated in a Qual VAR as an endogenous variable. In addition, the Qual VAR is inherently a nonlinear model as it allows the oil and stock returns to behave as nonlinear functions of their own past values around business cycle turning points. Our results show that, for both oil and stock returns, the Qual VAR model outperforms the random walk model (in a statistically significant way) at all the forecasting horizons considered, i.e., one- to twelve-months-ahead. In addition, the Qual VAR model, also outperforms the AR and VAR models (in a statistically significant manner) at medium- to long-run horizons for oil returns, and short- to medium-run horizons for stock returns.

Suggested Citation

  • Rangan Gupta & Mark E. Wohar, 2015. "Forecasting Oil and Stock Returns with a Qual VAR using over 150 Years of Data," Working Papers 201589, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201589
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    Cited by:

    1. Riza Demirer & Rangan Gupta & Qiang Ji & Aviral Kumar Tiwari, 2018. "Geopolitical Risks and the Predictability of Regional Oil Returns and Volatility," Working Papers 201860, University of Pretoria, Department of Economics.
    2. repec:ebl:ecbull:eb-18-00237 is not listed on IDEAS
    3. repec:eee:eneeco:v:71:y:2018:i:c:p:62-69 is not listed on IDEAS
    4. Konstantinos Gkillas & Rangan Gupta & Mark E. Wohar, 2018. "Oil Shocks and Volatility Jumps," Working Papers 201825, University of Pretoria, Department of Economics.
    5. Afees A. Salisu & Raymond Swaray & Tirimisyu F. Oloko, 2017. "A multi-factor predictive model for oil-US stock nexus with persistence, endogeneity and conditional heteroscedasticity effects," Working Papers 024, Centre for Econometric and Allied Research, University of Ibadan.
    6. repec:gam:jsusta:v:11:y:2019:i:9:p:2482-:d:226587 is not listed on IDEAS
    7. Ramzi Benkraiem & Thi hong van Hoang & Amine Lahiani & Anthony Miloudi, 2018. "Crude oil and equity markets in major European countries: New evidence," Economics Bulletin, AccessEcon, vol. 38(4), pages 2094-2110.
    8. Bos, Martijn & Demirer, Riza & Gupta, Rangan & Tiwari, Aviral Kumar, 2018. "Oil returns and volatility: The role of mergers and acquisitions," Energy Economics, Elsevier, vol. 71(C), pages 62-69.
    9. Hardik A. Marfatia & Rangan Gupta & Esin Cakan, 2019. "Dynamic Impact of the U.S. Monetary Policy on Oil Market Returns and Volatility," Working Papers 201916, University of Pretoria, Department of Economics.
    10. repec:eee:ecofin:v:45:y:2018:i:c:p:206-214 is not listed on IDEAS
    11. Gupta, Rangan & Yoon, Seong-Min, 2018. "OPEC news and predictability of oil futures returns and volatility: Evidence from a nonparametric causality-in-quantiles approach," The North American Journal of Economics and Finance, Elsevier, vol. 45(C), pages 206-214.
    12. repec:eee:eneeco:v:71:y:2018:i:c:p:114-127 is not listed on IDEAS
    13. Rangan Gupta & Patrick Kanda & Aviral Kumar Tiwari & Mark E. Wohar, 2018. "Time-Varying Predictability of Oil Market Movements Over a Century of Data: The Role of US Financial Stress," Working Papers 201848, University of Pretoria, Department of Economics.
    14. repec:gam:jeners:v:11:y:2018:i:11:p:3029-:d:180549 is not listed on IDEAS

    More about this item

    Keywords

    Vector Autoregressions; Business Cycle Turning Points; Forecasting; Oil and Stock Prices;

    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
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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