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Economic Policy Uncertainty and Long-Run Stock Market Volatility and Correlation

Author

Listed:
  • Hossein Asgharian

    (Lund University)

  • Charlotte Christiansen

    (Aarhus University and CREATES)

  • Ai Jun Hou

    (Stockholm Business School)

Abstract

We use Baker, Bloom, and Davis’s (2016) economic policy uncertainty indices in combination with the mixed data sampling (MIDAS) approach to investigate long-run stock market volatility and correlation, primarily for the US and UK. Long-run US–UK stock market correlation depends positively on US economic policy uncertainty shocks. The dependence is asymmetric, with only positive shocks - increasing uncertainty - being of importance. The US long-run stock market volatility depends significantly on US economic policy uncertainty shocks but not on UK shocks, while the UK long-run stock market volatility depends significantly on both. Allowing for US economic policy uncertainty shocks improves the out-of-sample forecasting of US–UK stock market correlation and enhances portfolio performance. Similar results apply to the long-run correlation between the US and Canada, China, and Germany.

Suggested Citation

  • Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou, 2017. "Economic Policy Uncertainty and Long-Run Stock Market Volatility and Correlation," CREATES Research Papers 2018-12, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2018-12
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    economic policy uncertainty index; mixed data sampling; stock market correlation; stock market volatility; asymmetry;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • 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

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