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Forecasting European Economic Policy Uncertainty

Author

Listed:
  • Stavros Degiannakis

    (Department of Economics and Regional Development, Panteion University of Social and Political Sciences)

  • George Filis

    (Department of Accounting, Finance and Economics, Bournemouth University)

Abstract
Forecasting the economic policy uncertainty in Europe is of paramount importance given the on-going debt crisis and the Brexit vote. This paper evaluates monthly out-of-sample economic policy uncertainty index forecasts and examines whether ultra-high frequency information from asset market volatilities and global economic policy uncertainty can improve the forecasts relatively to the no-change forecast. The results show that the global economic policy uncertainty provides the highest predictive gains, followed by the European and US stock market volatilities. The results hold true even when we consider the directional accuracy.

Suggested Citation

  • Stavros Degiannakis & George Filis, 2018. "Forecasting European Economic Policy Uncertainty," BAFES Working Papers BAFES15, Department of Accounting, Finance & Economic, Bournemouth University.
    • Handle: RePEc:bam:wpaper:bafes15
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Chatziantoniou, Ioannis & Degiannakis, Stavros & Delis, Panagiotis & Filis, George, 2021. "Forecasting oil price volatility using spillover effects from uncertainty indices," Finance Research Letters, Elsevier, vol. 42(C).
    2. Chatziantoniou, Ioannis & Degiannakis, Stavros & Delis, Panagiotis & Filis, George, 2019. "Can spillover effects provide forecasting gains? The case of oil price volatility," MPRA Paper 96266, University Library of Munich, Germany.
    3. Brandt, Richard, 2021. "Economic Policy Uncertainty Index: Extension and optimization of Scott R. Baker, Nicholas Bloom and Steven J. Davis's search term," DoCMA Working Papers 5, TU Dortmund University, Dortmund Center for Data-based Media Analysis (DoCMA).
    4. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2021. "Point and density forecasting of macroeconomic and financial uncertainties of the USA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 700-707, July.
    5. Degiannakis, Stavros & Filis, George, 2023. "Oil price assumptions for macroeconomic policy," Energy Economics, Elsevier, vol. 117(C).
    6. Bonato, Matteo & Çepni, Oğuzhan & Gupta, Rangan & Pierdzioch, Christian, 2021. "Do oil-price shocks predict the realized variance of U.S. REITs?," Energy Economics, Elsevier, vol. 104(C).
    7. Gupta, Rangan & Sun, Xiaojin, 2020. "Forecasting economic policy uncertainty of BRIC countries using Bayesian VARs," Economics Letters, Elsevier, vol. 186(C).
    8. Alqahtani, Abdullah & Klein, Tony & Khalid, Ali, 2019. "The impact of oil price uncertainty on GCC stock markets," Resources Policy, Elsevier, vol. 64(C).
    9. Orlowski, Lucjan T., 2023. "How susceptible is the European financial stability to economic policy uncertainty?," Journal of Policy Modeling, Elsevier, vol. 45(4), pages 864-875.
    10. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2020. "Point and Density Forecasting of Macroeconomic and Financial Uncertainties of the United States," Working Papers 202058, University of Pretoria, Department of Economics.
    11. Joscha Beckmann & Robert L. Czudaj & Gary Koop, 2019. "An empirical assessment of recent challenges in today's financial markets," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 1-4, February.
    12. Syed Jawad Hussain Shahzad & Rangan Gupta & Riza Demirer & Christian Pierdzioch, 2022. "Oil shocks and directional predictability of macroeconomic uncertainties of developed economies: Evidence from high‐frequency data†," Scottish Journal of Political Economy, Scottish Economic Society, vol. 69(2), pages 169-185, May.

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    More about this item

    Keywords

    Economic policy uncertainty; forecasting; financial markets; commodities markets; HAR; ultra-high frequency information;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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