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Time-Varying Predictability of Oil Market Movements Over a Century of Data: The Role of US Financial Stress

Author

Listed:
  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Patrick Kanda

    (THEMA, Université de Cergy-Pontoise)

  • Aviral Kumar Tiwari

    (Montpellier Business School, Montpellier, France)

  • Mark E. Wohar

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

Abstract

In this paper we analyze whether a news-based measure of financial stress index (FSI) in the US can predict West Texas Intermediate oil returns and (realized) volatility over the monthly period of 1889:01 to 2016:12, using a dynamic conditional correlation multivariate generalized autoregressive conditional heteroscedasticity (DCC-MGARCH) model. Our results show that, standard linear Granger causality test fails to detect any evidence of predictability. However, the linear model is found to be misspecified due to structural breaks and nonlinearity, and hence, the result of no causality from FSI to oil returns and volatility cannot be considered reliable. When we use the DCC-MGARCH model, which is robust to such misspecifications, in 75 percent and 80 percent of the sample periods, FSI in fact do strongly predict the oil returns and volatility respectively. Overall, our results highlight that FSI is helpful in predicting oil returns and volatility, when one accounts for nonlinearity and regime changes through a robust time-varying model.

Suggested Citation

  • 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.
    • Handle: RePEc:pre:wpaper:201848
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    Cited by:

    1. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    2. Cai, Yifei & Chang, Hao-Wen & Chang, Tsangyao, 2023. "Evaluating time-varying granger causality between US-China political relation changes and China stock market," Finance Research Letters, Elsevier, vol. 55(PA).
    3. Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment," Energies, MDPI, vol. 14(23), pages 1-18, December.
    4. Hong, Yanran & Li, Pan & Wang, Lu & Zhang, Yaojie, 2023. "New evidence of extreme risk transmission between financial stress and international crude oil markets," Research in International Business and Finance, Elsevier, vol. 64(C).
    5. Hu, Genhua & Jiang, Haifeng, 2023. "Time-varying jumps in China crude oil futures market impacted by COVID-19 pandemic," Resources Policy, Elsevier, vol. 82(C).
    6. Yang Hu & Yanran Hong & Kai Feng & Jikai Wang, 2023. "Evaluating the Importance of Monetary Policy Uncertainty: The Long- and Short-Term Effects and Responses," Evaluation Review, , vol. 47(2), pages 264-286, April.
    7. Zhang, Hongwei & Wang, Peijin, 2021. "Does Bitcoin or gold react to financial stress alike? Evidence from the U.S. and China," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 629-648.
    8. Ozcelebi, Oguzhan, 2020. "Assessing the impacts of financial stress index of developed countries on the exchange market pressure index of emerging countries," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 288-302.
    9. Dagher, Leila & Hasanov, Fakhri J., 2023. "Oil market shocks and financial instability in Asian countries," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 182-195.
    10. Caporina, Massimiliano & Costola, Michele, 2021. "Time-varying granger causality tests for applications in global crude oil markets: A study on the DCC-MGARCH Hong test," SAFE Working Paper Series 324, Leibniz Institute for Financial Research SAFE.
    11. Wan, Xiaoli & Yan, Yuruo & Zeng, Zhixiong, 2020. "Exchange rate regimes and market integration: evidence from the dynamic relations between renminbi onshore and offshore markets," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    12. Dutta, Anupam & Soytas, Ugur & Das, Debojyoti & Bhattacharyya, Asit, 2022. "In search of time-varying jumps during the turmoil periods: Evidence from crude oil futures markets," Energy Economics, Elsevier, vol. 114(C).
    13. Hong, Yanran & Wang, Lu & Liang, Chao & Umar, Muhammad, 2022. "Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework," Resources Policy, Elsevier, vol. 77(C).
    14. Taicir Mezghani & Mouna Boujelbène Abbes, 2023. "Forecast the Role of GCC Financial Stress on Oil Market and GCC Financial Markets Using Convolutional Neural Networks," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(3), pages 505-530, September.
    15. Yang, Kun & Wei, Yu & Li, Shouwei & Liu, Liang & Wang, Lei, 2021. "Global financial uncertainties and China’s crude oil futures market: Evidence from interday and intraday price dynamics," Energy Economics, Elsevier, vol. 96(C).
    16. Das, Debojyoti & Maitra, Debasish & Dutta, Anupam & Basu, Sankarshan, 2022. "Financial stress and crude oil implied volatility: New evidence from continuous wavelet transformation framework," Energy Economics, Elsevier, vol. 115(C).
    17. Caporin, Massimiliano & Costola, Michele, 2022. "Time-varying Granger causality tests in the energy markets: A study on the DCC-MGARCH Hong test," Energy Economics, Elsevier, vol. 111(C).

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    More about this item

    Keywords

    US Financial Stress Index; DCC-MGARCH; WTI Oil Returns; Realized Volatility;
    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
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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