IDEAS home Printed from https://ideas.repec.org/p/emu/wpaper/15-02.pdf.html
   My bibliography  Save this paper

The Role Of News-Based Uncertainty Indices In Predicting Oil Markets: A Hybrid Nonparametric Quantile Causality Method

Author

Listed:
  • Mehmet Balcilar

    (Department of Economics, Eastern Mediterranean University)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

  • STELIOS BEKIROS

    (European University Institute (EUI))

Abstract

We emphasize the role of news-based economic policy and equity market uncertainty indices as robust drivers of oil price fluctuations. In that, we utilize a new hybrid nonparametric quantile causality methodology in order to investigate whether EPU and EMU uncertainty measures incorporate critical predictability for oil market returns and volatility. Based on an updated daily database spanning January 1986 to December 2014, we find that both measures present strong predictability over the entire distribution of oil around the median, yet more importantly for volatility forecastability covers the entire distribution except minor divergences in the tails. Therefore, an inherent heterogeneity is observed and an asymmetric pattern over the distribution of oil returns and its volatility exists with respect to uncertainty predictability.

Suggested Citation

  • Mehmet Balcilar & Rangan Gupta & STELIOS BEKIROS, 2015. "The Role Of News-Based Uncertainty Indices In Predicting Oil Markets: A Hybrid Nonparametric Quantile Causality Method," Working Papers 15-02, Eastern Mediterranean University, Department of Economics.
    • Handle: RePEc:emu:wpaper:15-02.pdf
    as

    Download full text from publisher

    File URL: http://repec.economics.emu.edu.tr/RePEc/emu/wpaper/15-02.pdf
    File Function: First version, 2015
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2014. "Dynamic Spillovers of Oil Price Shocks and Policy Uncertainty," Department of Economics Working Paper Series 166, WU Vienna University of Economics and Business.
    2. Jones, Paul M. & Olson, Eric, 2013. "The time-varying correlation between uncertainty, output, and inflation: Evidence from a DCC-GARCH model," Economics Letters, Elsevier, vol. 118(1), pages 33-37.
    3. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    4. Kang, Wensheng & Ratti, Ronald A., 2013. "Structural oil price shocks and policy uncertainty," Economic Modelling, Elsevier, vol. 35(C), pages 314-319.
    5. Bekiros, Stelios & Gupta, Rangan & Paccagnini, Alessia, 2015. "Oil price forecastability and economic uncertainty," Economics Letters, Elsevier, vol. 132(C), pages 125-128.
    6. Colombo, Valentina, 2013. "Economic policy uncertainty in the US: Does it matter for the Euro area?," Economics Letters, Elsevier, vol. 121(1), pages 39-42.
    7. Kang, Wensheng & Ratti, Ronald A., 2013. "Oil shocks, policy uncertainty and stock market return," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 305-318.
    8. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    9. Kang, Wensheng & Ratti, Ronald A. & Yoon, Kyung Hwan, 2015. "The impact of oil price shocks on the stock market return and volatility relationship," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 34(C), pages 41-54.
    10. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    11. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2016. "Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data," Energy Economics, Elsevier, vol. 56(C), pages 117-133.
    12. Hamilton, James D, 1983. "Oil and the Macroeconomy since World War II," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 228-248, April.
    13. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    14. Nishiyama, Yoshihiko & Hitomi, Kohtaro & Kawasaki, Yoshinori & Jeong, Kiho, 2011. "A consistent nonparametric test for nonlinear causality—Specification in time series regression," Journal of Econometrics, Elsevier, vol. 165(1), pages 112-127.
    15. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2014. "Dynamic spillovers of oil price shocks and economic policy uncertainty," Energy Economics, Elsevier, vol. 44(C), pages 433-447.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bonaccolto, G. & Caporin, M. & Gupta, R., 2018. "The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 446-469.
    2. Aloui, Riadh & Gupta, Rangan & Miller, Stephen M., 2016. "Uncertainty and crude oil returns," Energy Economics, Elsevier, vol. 55(C), pages 92-100.
    3. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2017. "Oil price shocks and policy uncertainty: New evidence on the effects of US and non-US oil production," Energy Economics, Elsevier, vol. 66(C), pages 536-546.
    4. Cai, Yifei & Wu, Yanrui, 2019. "Time-varied causality between US partisan conflict shock and crude oil return," Energy Economics, Elsevier, vol. 84(C).
    5. Balcilar, Mehmet & Gupta, Rangan & Kim, Won Joong & Kyei, Clement, 2019. "The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 150-163.
    6. Li, Lei & Yin, Libo & Zhou, Yimin, 2016. "Exogenous shocks and the spillover effects between uncertainty and oil price," Energy Economics, Elsevier, vol. 54(C), pages 224-234.
    7. Yingce Yang & Junjie Guo & Ruihong He, 2023. "The Asymmetric Impact of the Oil Price and Disaggregate Shocks on Economic Policy Uncertainty: Evidence From China," SAGE Open, , vol. 13(2), pages 21582440231, June.
    8. Gilles Dufrénot & William Ginn & Marc Pourroy, 2023. "ENSO Climate Patterns on Global Economic Conditions," AMSE Working Papers 2308, Aix-Marseille School of Economics, France.
    9. Stavros Degiannakis & George Filis, 2019. "Forecasting European economic policy uncertainty," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 94-114, February.
    10. Hong, Yun & Zhang, Rushan & Zhang, Feipeng, 2024. "Time-varying causality impact of economic policy uncertainty on stock market returns: Global evidence from developed and emerging countries," International Review of Financial Analysis, Elsevier, vol. 91(C).
    11. Degiannakis, Stavros & Filis, George & Panagiotakopoulou, Sofia, 2018. "Oil price shocks and uncertainty: How stable is their relationship over time?," Economic Modelling, Elsevier, vol. 72(C), pages 42-53.
    12. Dash, Saumya Ranjan & Maitra, Debasish, 2021. "Do oil and gas prices influence economic policy uncertainty differently: Multi-country evidence using time-frequency approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 397-420.
    13. Yong Jiang & Yi-Shuai Ren & Chao-Qun Ma & Jiang-Long Liu & Basil Sharp, 2018. "Does the price of strategic commodities respond to U.S. Partisan Conflict?," Papers 1810.08396, arXiv.org, revised Feb 2020.
    14. Sheng, Xin & Gupta, Rangan & Ji, Qiang, 2020. "The impacts of structural oil shocks on macroeconomic uncertainty: Evidence from a large panel of 45 countries," Energy Economics, Elsevier, vol. 91(C).
    15. Lee, Chien-Chiang & Olasehinde-Williams, Godwin & Özkan, Oktay, 2023. "Geopolitical oil price uncertainty transmission into core inflation: Evidence from two of the biggest global players," Energy Economics, Elsevier, vol. 126(C).
    16. Bekiros, Stelios & Gupta, Rangan & Paccagnini, Alessia, 2015. "Oil price forecastability and economic uncertainty," Economics Letters, Elsevier, vol. 132(C), pages 125-128.
    17. Kang, Wensheng & de Gracia, Fernando Perez & Ratti, Ronald A., 2019. "The asymmetric response of gasoline prices to oil price shocks and policy uncertainty," Energy Economics, Elsevier, vol. 77(C), pages 66-79.
    18. Adeniyi Adeosun, Opeoluwa & Anagreh, Suhaib & Tabash, Mosab I. & Adedokun, Adebayo, 2023. "Revisiting the connectedness between oil prices and uncertainty indicators in BRICS countries," Resources Policy, Elsevier, vol. 86(PA).
    19. F. Dilvin Taşkin & Efe Çağlar Çağlı & Umut Halaç, 2016. "The impact of oil price shocks on the volatility of the Turkish stock market," International Journal of Accounting and Finance, Inderscience Enterprises Ltd, vol. 6(1), pages 1-23.
    20. Yuan, Di & Li, Sufang & Li, Rong & Zhang, Feipeng, 2022. "Economic policy uncertainty, oil and stock markets in BRIC: Evidence from quantiles analysis," Energy Economics, Elsevier, vol. 110(C).

    More about this item

    Keywords

    Uncertainty; Oil markets; Volatility; Quantile causality;
    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
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:emu:wpaper:15-02.pdf. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Mehmet Balcilar (email available below). General contact details of provider: https://edirc.repec.org/data/deemuty.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.