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

Author

Listed:
  • Elie Bouri

    () (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon)

  • Konstantinos Gkillas

    () (Department of Business Administration, University of Patras – University Campus, Rio, P.O. Box 1391, 26500 Patras, Greece)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Christian Pierdzioch

    () (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)

Abstract

We analyze the role of the US-China trade war in predicting, both in- and 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 predictor 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 which 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, 2020. "Forecasting Realized Volatility of Bitcoin: The Role of the Trade War," Working Papers 202003, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:202003
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    Cited by:

    1. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, "undated". "Investor Confidence and Forecastability of US Stock Market Realized Volatility : Evidence from Machine Learning," Working Papers 202118, University of Pretoria, Department of Economics.
    2. Periklis Gogas & Theophilos Papadimitriou, 2021. "Machine Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 1-4, January.
    3. Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2020. "Forecasting Realized Stock-Market Volatility: Do Industry Returns have Predictive Value?," Working Papers 2020107, University of Pretoria, Department of Economics.

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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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