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Uncertainty shocks of Trump election in an interval model of stock market

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  • Yuying Sun
  • Kenan Qiao
  • Shouyang Wang

Abstract

This paper proposes a new class of nonlinear interval models for interval-valued time series. By matching the interval model with interval observations, we develop a nonlinear minimum-distance estimation method for the proposed models, and establish the asymptotic theory for the proposed estimators. Superior to traditional point-based methods, the proposed interval modelling approach can assess the change in both the trend and volatility simultaneously. Within the proposed interval framework, this paper examines the impact of the 2016 US presidential election (henceforth Trump election) on the US stock market as a case study. Considering the validity of daily high-low range as a proxy of market efficiency, we employ an interval-valued return to jointly measure the fundamental value movement and market efficiency simultaneously. Empirical results suggest a strong evidence that the Trump election has increased the level/trend and lowered the volatility of the S&P 500 index in both ex ante and ex post analysis. Furthermore, a longer half-life period for the impact on fundamental value (62.4 days) than high-low range (15.9 days) has shown that the impact of Trump's victory on fundamental value is more persistent than its impact on market efficiency.

Suggested Citation

  • Yuying Sun & Kenan Qiao & Shouyang Wang, 2021. "Uncertainty shocks of Trump election in an interval model of stock market," Quantitative Finance, Taylor & Francis Journals, vol. 21(5), pages 865-879, May.
    • Handle: RePEc:taf:quantf:v:21:y:2021:i:5:p:865-879
    DOI: 10.1080/14697688.2020.1800070
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    Cited by:

    1. Delia DiaconaÅŸu & Seyed Mehdian & Ovidiu Stoica, 2023. "The Global Stock Market Reactions to the 2016 U.S. Presidential Election," SAGE Open, , vol. 13(2), pages 21582440231, June.
    2. Sun, Yuying & Zhang, Xinyu & Wan, Alan T.K. & Wang, Shouyang, 2022. "Model averaging for interval-valued data," European Journal of Operational Research, Elsevier, vol. 301(2), pages 772-784.
    3. Piao Wang & Shahid Hussain Gurmani & Zhifu Tao & Jinpei Liu & Huayou Chen, 2024. "Interval time series forecasting: A systematic literature review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 249-285, March.
    4. Teresa Perry, 2023. "Did the 2016 election cause changes in substance use? An intersectional approach," Economics and Politics, Wiley Blackwell, vol. 35(3), pages 1020-1069, November.
    5. Zhu, Yichen & Ghoshray, Atanu, 2021. "Climate Anomalies and Its Impact on U.S. Corn and Soybean Prices," 2021 Conference, August 17-31, 2021, Virtual 315271, International Association of Agricultural Economists.

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