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Forecasting Realized Volatility of Bitcoin: The Role of the Trade War

Author

Listed:
  • Elie Bouri

    (Holy Spirit University of Kaslik)

  • Konstantinos Gkillas

    (University of Patras)

  • Rangan Gupta

    (University of Pretoria)

  • Christian Pierdzioch

    (Helmut Schmidt University)

Abstract

We analyze the role of the US–China trade war in forecasting out-of-sample daily realized volatility of Bitcoin returns. We study intraday data spanning from 1st July 2017 to 30th June 2019. We use the heterogeneous autoregressive realized volatility model (HAR-RV) as the benchmark model to capture stylized facts such as heterogeneity and long-memory. We then extend the HAR-RV model to include a metric of US–China trade tensions. This is our primary forecasting variable of interest, and it is based on Google Trends. We also control for jumps, realized skewness, and realized kurtosis. For our empirical analysis, we use a machine-learning technique that is known as random forests. Our findings reveal that US–China trade uncertainty does improve forecast accuracy for various configurations of random forests and forecast horizons.

Suggested Citation

  • Elie Bouri & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting Realized Volatility of Bitcoin: The Role of the Trade War," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 29-53, January.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:1:d:10.1007_s10614-020-10022-4
    DOI: 10.1007/s10614-020-10022-4
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    More about this item

    Keywords

    Bitcoin; Realized volatility; Trade war; Random forests;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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